Hugging Face launches Microduck open-source robot at $399
→ 25cm duck with camera, lidar, IMUs learns new tricks via RL in simulation then deployed
→ What acquisition of Pollen Robotics signals: democratizing physical AI through affordable open-source hardware

Gobano’s Toutatis RL engine achieves 99%+ reliability from 10-200 demos
→ Task-specific world model + off-policy RL on frozen embeddings reaches 99/100 on towel folding, 31/31 on zip insertion
→ Why splitting Reliability (repeated success) vs Performance (speed) modes matters: conventional RL blends both, masking rare failures

Gatik raises $200M following PepsiCo multiyear deal
→ 41 driverless box trucks moving Frito-Lay products in Dallas, Phoenix, Arkansas; $600M contracted revenue locked
→ Why middle-mile niche (dynamic routes up to 400 miles) succeeds where robotaxis/long-haul competed

Teradyne sues JAKA German subsidiary for patent infringement
→ Multiple patents covering hardware and software in force/power-limited arms; Unified Patent Court ruling covers 17 EU states
→ Why takedown signal matters more than compensation: protecting safety standards around cobots vs copycat products

Nori launches A3 household robot at $1,688
→ Wheeled platform with 2-claw arms, 8-hour runtime, owner-trained skills via desktop app or code
→ What no teleoperation + community skill marketplace reveal: developer-first approach vs fixed capabilities

Hugging Face is selling a $399 open source duck robot! 🦆

Hugging Face has unveiled the Microduck, a duck-like robot that sells for $399 and ships before Christmas. CEO Clem Delangue described it as an open-source robot you can teach new tricks with reinforcement learning. The 25-centimetre-tall duck can waddle, pick things up with its beak, get back up when it falls, crouch, and roller skate. “Welcome to the era of open-source affordable robots to democratize physical AI and world models,” Delangue said.

The Microduck perceives the world through a camera, lidar sensors, and two IMUs. Pollen Robotics notes that behaviours can be trained in simulation and deployed directly onto the robot, after which developers can fine-tune, retrain, and redeploy. The SDK, simulation, and full RL training stack are on GitHub.

Hugging Face is best known as the platform where developers go for open model weights, but it acquired French startup Pollen Robotics in April 2025 to build affordable open source AI hardware. The two launched Reachy Mini a couple of months later. They currently sell the $499 Reachy Mini, powered by a Raspberry Pi, and the $399 Reachy Mini Lite, which runs off a Mac or PC.

On the privacy question of putting a camera-equipped robot in your bedroom, Delangue has previously argued that bots running open source models are better than a black box system controlled by a few organisations. Open source does give developers auditability and control, though it doesn’t guarantee sensitive data stays private once consumers install third-party apps on top, since those apps can access cameras and microphones and send data elsewhere depending on how they’re built.

The launch lands while Hugging Face is reportedly set to be acquired by Nvidia at a $13 billion valuation. The two have partnered for years, with Nvidia providing infrastructure since at least 2023, and both have publicly pushed for open source AI.

Read more here!

Gobano introduces Toutatis, an RL engine for getting robots to 99%+ reliability! 🎯

Gobano Robotics has released Toutatis v1, a reinforcement learning engine that takes 10 to 200 human demonstrations and turns them into reliability-optimised controllers in days. The framing is about what separates a lab result from a production system: a robot that does a task once proves capability, but one that does it shift after shift creates value. At 95% success you get 50 failures per 1,000 operations. At 99% you get 10. Production wants 99.9%, or roughly one.

The core argument is that demonstration data teaches a robot what people did, not what maximises success or eliminates the rare failures that stop a system running unsupervised. Gobano draws the analogy to language models: pretraining creates capability, RL converts it into performance. They point to DeepSeek-R1-Zero, where pure RL from a pretrained base produced reflection and verification behaviours without supervised reasoning examples. Their thesis is that RL is both necessary and sufficient for robot deployment, because deployment is defined by outcomes rather than demonstrations.

Technically, Toutatis trains a task-specific world model from the same demonstrations used for the imitation policy. The model builds a compact latent representation of what’s in the scene, how it changes under an action, which transitions create progress, and where failures emerge. Gobano then freezes the visual encoder and runs off-policy RL directly on those cached embeddings, which made training roughly 10x faster than their equivalent end-to-end visual RL setup. Two timescales run in parallel: fast policy updates on the frozen representation, and periodic world-model refreshes as real-world coverage expands.

One design choice worth noting is the split between “Reliability” and “Performance” training modes. Conventional RL maximises average task reward, which blends speed and success, so the average can rise while rare failures persist. Reliability mode prioritises repeated successful completion until the acceptance threshold is hit; Performance mode then rewards faster completion while protecting that reliability level.

On the Robomimic benchmark, Toutatis hit 1,000/1,000 on Square and 998/1,000 on Tool Hang over trailing windows of 1,000 rollouts. On real hardware, ball picking went from 11 demonstrations and an initial 24/100 to 108/108 across 854 rollouts. Towel folding started from 135 demonstrations, needed 215 more targeted ones to cover harder configurations, and finished at 99/100 after 2,633 rollouts. Zip-tie insertion reached 31/31 after 3,375 insertion-focused rollouts. The models are small enough to train on a gaming PC and run locally on a gaming laptop, with inference around 3 ms for ball picking and 10 ms for towel folding. Gobano is already using Toutatis in deployments and plans to open the system publicly.

Read more here!

Self-driving truck startup Gatik raises $200M following PepsiCo deal! 🚚

Gatik has raised $200 million, two months after signing a multiyear commercial agreement with PepsiCo. The round was led by Qatar Investment Authority and Koch Disruptive Technologies, with Millennium Management, ARK Invest, and Intact Private Capital participating. The Santa Clara company has now raised about $500 million since coming out of stealth in 2019 and didn’t disclose its valuation.

“These are all long-term investors,” said co-founder and CEO Gautam Narang. “All the scale-up plans that we have and the growth that we have in mind for the next few years, these are the kind of financial partners that can help us.”

Gatik picked a lane nobody else wanted. While dozens of companies chased robotaxis, sidewalk robots, long-haul big rigs, and mining vehicles, Gatik built driverless box trucks with Isuzu Motors for short-haul delivery. It started with fixed routes under 10 miles and grew to dynamic routes with dozens of pick-up and drop-off points covering up to 400 miles. That middle-mile niche is now largely its own, with customers including Loblaws, Kroger, Tyson Foods, and Walmart, its first customer disclosed six years ago.

Last year the company pulled safety drivers from its commercial routes. It now runs dozens of fully driverless trucks commercially across several markets, with third-generation vehicles operating around the clock on surface streets and highways in light rain and snow. The largest public partnership is with PepsiCo: 41 driverless box trucks moving Cheetos, Doritos, and other Frito-Lay products from distribution centres to stores in Dallas, Phoenix, and Northwest Arkansas.

Narang says the company has locked in commercial deals worth $600 million in contracted revenue. The new funding goes toward hiring engineers and operational staff beyond its current 350-person workforce, and expanding into new markets, potentially outside North America eventually.

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Teradyne Robotics ramps up its fight against cobot copycats! ⚖️

Teradyne Robotics, parent company of Universal Robots, has filed a patent infringement lawsuit against the German subsidiary of Chinese robotics company JAKA. The case alleges JAKA infringed patents covering both hardware and software used in Universal Robots’ cobots. It’s Teradyne’s second major case against a Chinese robotics company in Europe this year, after suing Elite Robots’ German subsidiary over copyright infringement in February. In April, the Regional Court of Hamburg issued a preliminary injunction barring Elite Robots Germany from offering the disputed software in Germany.

The JAKA case works differently. “This is a patent case where the other one was about copyright,” David Brandt, VP of R&D and CTO at Universal Robots, told The Robot Report. “It’s multiple patents this time, and they cover both significant hardware and software aspects of our products.” Teradyne became interested after noticing similarities between the two companies’ force- and power-limited arms. “Our robots and the robots of JAKA look quite alike,” Brandt said. “We then chose to acquire one of the robots and take a deeper look at it. Then it became quite obvious that there’s possible patent infringement in this.”

The case was filed with the Unified Patent Court Local Division in Copenhagen, since the alleged infringement occurred in Denmark and other EU member states. A ruling could take effect across 17 of the 18 EU member states participating in the UPC, plus certain countries outside the agreement including the UK and Spain. JAKA did not respond to a request for comment.

Brandt was clear the decision wasn’t about JAKA’s Chinese ownership. “To us, it’s not about China. It’s about other companies copying stuff that we designed, whether they’re Chinese or not.” He also acknowledged the basic difficulty of protecting a robot once it’s shipped: a competitor can buy one, take it apart, and study the design. The goal isn’t compensation. “Ideally, we would like them to stop copying and infringing our IP. This is more to send a very clear signal.”

There’s a safety argument too. Universal Robots helped establish the market for robots that work near people under proper risk assessment, and Brandt worries that copycat products not meeting those standards could hurt someone and reflect badly on the whole industry, pushing regulators to treat cobots as a safety problem.

Teradyne Robotics posted $100 million in Q2 2026 revenue, up from $91 million in Q1 and 33% higher year over year, marking its fifth consecutive quarter of growth. Universal Robots has sold more than 100,000 cobots. Brandt suggested JAKA won’t be the last target: “We do not see this as a one-time thing. JAKA is not the only robot out there that looks suspiciously like ours.”

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Nori Robotics launches a $1,688 household robot! 🔧

Nori Robotics, a Y Combinator-backed San Francisco startup, is selling the A3, a wheeled household robot for $1,688. The company says it can help cook, fetch items from the fridge, put things away, load the dishwasher, fold clothes, and pour ingredients. It has two arms with claws that lift a couple of pounds each, uses LiDAR and cameras for navigation, and runs up to 8 hours on a full charge. The first batch, roughly a hundred units, has already sold out.

The catch is who it’s for. Nori describes the A3 as a robot “built for people who want to actually build with one.” Unlike a robot vacuum with a fixed set of capabilities, the A3 is meant to be trained by its owners, who can then package that training into skills and share them with the Nori community through an online marketplace. Owners can either write code or train it via a desktop app that simulates the robot’s movements on screen. That opens it up beyond housework: one demo shows it flipping pages of sheet music while a guitarist plays.

That approach cuts both ways. You probably won’t get much functionality out of the box, or any guarantee the robot performs tasks without mistakes. But it may also mean the A3 doesn’t lean on teleoperation, where a human watches through the robot’s cameras and remotely drives it through the task, which is both less impressive and more intrusive than most buyers expect from a home robot.

Price-wise it undercuts everything else in the category by a wide margin. Weave Robotics’ Isaac 1 costs $8,000 and ships this fall. The bipedal 1X NEO is $20,000 and arrives next year. Nori also says most of the A3’s parts can be 3D printed and replaced by owners.

“We’re excited to see what developers, researchers, and teams build when capable humanoid hardware becomes this accessible,” said founder Antonio Li. A second batch of 100 units is open for order now, shipping this fall.

Read more here about it!

NVIDIA acquires Hugging Face for $12.9B
→ Open-source AI platform consolidation as chip dominance faces erosion (OpenAI, Google, Amazon, Anthropic building own chips)
→ Why open-source ecosystem on NVIDIA hardware matters more than closed labs for customer optionality

Figure launches Index for crowdsourced robot data
→ 264K downloads, 44K weekly active users, 16M videos in 4 months; 30 mins/second upload pace (4.9 years daily)
→ What 373 unique tasks, 1,146 objects, 116 environments per 1K hours reveal: diversity beats vendor throughput

SoftBank in talks for 1X majority stake at $6B valuation
→ Control purchase after selling Boston Dynamics stake, anchoring Skild AI $1.4B, acquiring ABB robotics $5.4B
→ Why balance sheet stability matters: funding production lines, World Model Lab compute, NEO delivery infrastructure

Skild AI releases S1 with video in-context learning
→ One set of weights executes unseen 10-minute tasks (plant potting, pancake cooking) from single demonstration, no fine-tuning
→ What 66% unseen task success at 100K hours vs 9% for language-prompted VLA reveals: video prompting worth 380 post-training episodes

Waymo planning Munich robotaxi service, public launch end 2027
→ Follows 11-city expansion playbook: manual mapping → testing with safety drivers → employee/guest access → public launch
→ Why Germany’s 5-year-old Level 4 framework attracts competitors (Mobileye, VW testing) vs London battleground with Wayve, Uber, Baidu

NVIDIA closes in on Hugging Face acquisition for $12.9B! 🤗

NVIDIA has agreed to buy Hugging Face for $12.9 billion, according to The Information. Business Insider, which first reported the takeover interest over the weekend, says the talks value the company at more than $13 billion but haven’t produced a signed agreement yet and could still fall apart. Neither company has commented, which is notable in NVIDIA’s case since it usually moves fast to correct reports it thinks are wrong.

Hugging Face, founded in 2016, is the main place developers share and download open-source AI models. Owning it would give NVIDIA a foothold in open-source AI at a moment when Nvidia’s chip dominance looks less secure than it did. OpenAI, Google, Amazon, and Anthropic are all building their own AI chips to reduce their dependence on Nvidia. A healthy open-source ecosystem gives customers alternatives to those closed labs, and those alternatives mostly run on Nvidia hardware. That’s the same logic behind NVIDIA already pouring tens of billions into its own open-source models.

Hugging Face CEO Clem Delangue has been publicly aligned with Nvidia’s open-source push all year. On CBS’s “Face the Nation” this month he described using an NVIDIA-modified version of a Chinese open-source model to defend against a cyberattack, and pointed to a letter signed by Jensen Huang and 24 other companies urging Washington to support open models rather than restrict them. In a CNBC interview in late July he warned that China is clearly dominating open-source AI.

There are other angles. Nvidia scaled back its DGX Cloud business about a year ago, and Hugging Face already helps developers run models on rented compute, offering a way back into that market. Nvidia has also promised to cover the cost of tens of billions in cloud deals for customers, and could resell unused capacity to Hugging Face’s users.

The price is a big jump. Hugging Face last raised $235 million in 2023 at a $4.5 billion valuation, in a round led by Salesforce Ventures with participation from GV, IBM Ventures, and NVIDIA. It also turned down a $500 million investment from NVIDIA late last year at a $7 billion valuation, saying it didn’t want a dominant investor swaying its decisions. Revenue is around $150 million a year, up from roughly $100 million two months earlier, and Delangue told TechCrunch last month the company is close to profitability. A $13 billion price would be a steep multiple on that, but a hard one to walk away from.

Read more here!

Figure comes out of stealth with Index, its physical data collection network! 📱

Figure has unveiled Index, an app-based pipeline for collecting robot training data from people around the world. The argument behind it is that the data needed to scale a general purpose robot doesn’t exist on the internet. It has to come from the real world, sampled across as many environments as possible.

The numbers from four months in stealth are substantial. Index has crossed 264,000 downloads across 108 countries with over 44,000 weekly active users. Creators, as Figure calls them, have uploaded more than 16 million videos. The app processes 30 minutes of video uploads every second, which works out to 4.9 years of human work uploaded daily. Figure has paid out $15 million to Creators so far and says it will spend over $1 billion on data and compute over the next 12 months.

Figure tried buying data first. Vendors couldn’t hit the throughput, diversity, or quality bar its Helix AI stack needed, so the company built the pipeline itself. Per 1,000 hours collected, Index contains 373 unique tasks, 1,146 unique manipulated objects, and 116 unique environments. The diversity comes from the people generating it: every new Creator brings an unfamiliar environment, unusual objects, and their own way of doing things. Figure says it has collected tasks as obscure as cleaning kitty litter, changing oil, and busing restaurant tables, alongside cooking, laundry, and work inside logistics centres, restaurants, factories, and offices.

Ingesting that much video meant rebuilding the data infrastructure around consumer app constraints: 24/7 availability, continuous large-scale compute, and real-time feedback to users. The pipeline runs five stages. Automated filters screen for technical, visual, and semantic quality. Human analysts audit samples at the user level for deliberate evasion attempts. Videos are embedded and discarded if they’re too similar to existing data. What remains gets rebalanced using task quotas and embedding-based clusters. Finally, hierarchical text captions are generated for every episode.

Anyone can sign up as a Creator and record tasks in their own home or workplace, or book a Creator through the app to come do chores at their house or business. Figure frames this as groundwork for ordering robots as a service: today a person comes to clean your house, eventually a robot does it.

Read more here!

SoftBank in talks to buy majority stake in 1X at $6 billion valuation! 💸

SoftBank is in talks to take a majority stake in 1X Technologies at a valuation of roughly $6 billion, according to The Information. A completed deal would hand Masayoshi Son controlling interest in one of the most prominent bipedal platforms in the West, and would mark a defining moment for 1X as it tries to move from research prototypes to hardware in people’s homes. Neither company has commented.

Last September, 1X was reported to be seeking up to $1 billion at a valuation of $10 billion or more. Against that, $6 billion reads as a markdown, but the comparison needs a caveat: the $10 billion was an ask that was never confirmed to have closed, and a majority purchase is priced differently from a minority round. Control usually trades at a premium to the venture mark, not a discount. What surrendering a controlling interest buys 1X is balance sheet stability, which matters when you’re funding low-volume production lines in Hayward, compute for the 1X World Model Lab, and support and logistics infrastructure ahead of NEO deliveries.

The reporting leaves the most consequential terms unaddressed. How large is “majority”? A 51% position and an 80% position mean very different things for 1X’s independence. Is $6 billion pre- or post-money? Is the money primary capital funding the company, or secondary cashing out existing holders? Is OpenAI, 1X’s earliest institutional backer, selling? And does founder Bernt Børnich keep operational control? None of it is public.

For SoftBank, the deal fits a broader consolidation of the robotics supply chain around what it treats as a split between brains and bodies. After selling its remaining Boston Dynamics stake to Hyundai for $325 million this summer, SoftBank anchored a $1.4 billion Series C in Skild AI for foundation models, bought ABB’s robotics division for $5.4 billion, and is reportedly in talks to anchor an $800 million round for Agile Robots. NEO covers the one category ABB’s arms can’t reach: compliant, quiet, tendon-driven hardware built to operate around people in unstructured homes.

The strongest argument that $6 billion undervalues 1X is demand. When preorders opened in October 2025 at $20,000 outright or $499 a month, the company reportedly sold out its entire first year of production in five days. The strongest argument the other way is that none of it has shipped. 1X promised initial NEO deliveries in 2026, and it’s now late August with no customer deliveries publicly confirmed. Closing that gap is exactly what SoftBank’s money would pay for, and exactly what makes the timing of a control deal worth watching.

Read more here!

Skild AI introduced S1: show it a video, and the robot does the task! 🎬

Skild AI has released S1, a robot foundation model built around in-context learning. Show it a video of a task, short or long, seen or unseen, and it executes. No fine-tuning, no post-training. One set of weights produced every result in the company’s announcement.

The framing is a comparison to language models. Skild argues robotics has been stuck in the BERT era, where each new application demands more data collection and another fine-tuning run. The shift from those early models to ChatGPT came from in-context learning, or prompting: a user introduces a novel concept through the prompt and the model handles it without touching the weights. Skild thinks that’s the whole point of pre-training in robotics too. Notably, S1 uses video demonstrations rather than language to specify tasks. Language works fine for atomic actions like “hand me the mug,” but nobody learns to fold a fitted sheet or whisk egg whites to stiff peaks from a sentence.

The headline results are on long-horizon tasks the model never saw during training: plant potting, pancake cooking, pour-over coffee, and kit assembly. These run up to ten minutes, span dozens of steps, and are driven by a single visual demonstration. S1 dug into soil to make room for a plant, pressed a coffee filter into a funnel, and flipped a pancake. Skild says this is the first time a robotics foundation model has shown in-context learning on tasks this long that weren’t in the pre-training set. For the plant potting task, the supplies arrived at the office at 8:54 PM and S1 was executing autonomously by 9:27 PM. The gap between recording the demonstration and autonomous execution was 11 minutes.

The scaling study is where the case gets made. Skild trained both in-context and language-conditioned policies on identical data and compute, from 1k to 100k hours. On tasks the models had seen, the two end up close: 96% for ICL against 89% for the language-prompted VLA. On unseen tasks the gap is enormous. At 100k hours, language prompting reaches 9% while ICL hits 66%. Expressed differently, one video demonstration is worth roughly 380 post-training episodes, which for tasks over four minutes means 50 to 100 hours of teleoperation.

Several behaviors emerged that weren’t explicitly trained. S1 kept working when objects were slid away mid-approach, swapped out, or the lighting changed. It retried after failures rather than blindly proceeding. When a prompt showed watering a plant with a watering can but only a cup was available, it used the cup. And it sometimes improves on flawed demonstrations: in one prompt the demonstrator dropped an egg and made a mess, while S1 did the same step with a controlled motion. It treats the demonstration as a specification of the goal, not a trajectory to copy.

Read more here about it!

Waymo robotaxis are headed to Munich! 🥨

Waymo plans to launch a robotaxi service in Munich, months after setting up an entity in Germany. It still has mapping, testing, and regulatory hurdles to clear before commercial operations can begin.

The rollout follows the same sequence Waymo has used in the other 11 cities where it operates commercially. First it manually drives its vehicles to map the streets. Then it tests autonomous vehicles with human safety drivers, before removing them. At that stage it usually opens rides to employees, media, and invited guests, then to limited public service, and finally to all riders once regulators sign off. Waymo says it’s working with Germany’s Federal Motor Transport Authority (KBA), state authorities, and local officials, and expects commercial ride-hailing to open to the public toward the end of 2027.

Germany was the first EU country to create a legal framework for Level 4 autonomous driving, adopting the legislation more than five years ago. That has made it a hotspot for AV testing, with Mobileye and Volkswagen among the companies holding permits. Most permit holders, including Wayve and Autobrains, are at Level 3, where the system handles driving in specific conditions but a driver must be ready to take over. Waymo currently holds no permits in the country.

Germany isn’t the only European battleground. London is shaping up as another one, with Waymo planning a commercial service there in 2026 that would put it against Wayve and Uber. Baidu has also begun testing in the city through its partnership with Lyft and Freenow.

Read more here about it!

Unitree shares close up 460% on Shanghai debut
→ 150.80 yuan offer to 845 yuan close, first mainland-listed humanoid maker
→ Why profitability (278M yuan net profit 2025) + volume (5,500 units) separates Unitree from hype

Veeda AI raises $90M seed from Radical, Khosla
→ Ex-NVIDIA researchers (Fidler, Ling, Gojcic) building world models for physical AI with global footprint
→ What “The Matrix for physical AI” reveals: simulating trial-and-error avoids costly real-world failures

Gravis Robotics lands $200M SoftBank Series A
→ ETH Zurich spinout retrofitting excavators across 8 manufacturers (Caterpillar, Volvo, John Deere, Hitachi) with same core software
→ Why mixed-fleet compatibility matters: 2/3 global heavy equipment outside top 3 makers, driven by existing fleet investments

Uber partners Zipline for one million daily drone deliveries by 2029
→ First Uber Eats drone drops by end 2026, expanding from Zipline markets to dozens of US cities
→ Why platform aggregation strategy (Zipline + Flytrex, no in-house) prioritizes breadth over control

Matic launches Cues voice and gesture control
→ Point at spill, say “clean this,” robot localizes and cleans autonomously across 75 languages
→ What 8x airflow vs competitors + 9-year development + WIRED 10/10 reveal: specialization in execution over novelty
→ 13K families using after years building, $115M raised with 6-month refund guarantee

Unitree shares close up 460% on Shanghai debut! 📈

Unitree Robotics listed on Shanghai’s STAR Market on Wednesday. Shares were offered at 150.80 yuan ($22.36) and closed the first day at 845 yuan, up more than 460%. Other Chinese humanoid makers have gone public before, but this is the first listing on the mainland.

The company was founded in 2016 and is officially called Yushu Technology. It shipped over 5,500 humanoid robots last year and turned a net profit of 278 million yuan in 2025, which almost nobody else in the sector can say. Price is its main weapon against US competitors. Unitree’s robot dogs start at $2,700. Boston Dynamics’ Spot costs around $70,000. Its child-sized G1 humanoid launched in 2024 at $13,500, and Tesla still hasn’t delivered a rival product.

Unitree is based in Hangzhou, part of a cluster of robotics firms that have taken in heavy government investment. The number of Chinese robotics companies more than tripled between 2020 and 2024. Fei Qin of the University of Bath said Beijing treats the sector as a strategic priority, partly because an ageing population points to a worker shortage. Leju Robotics and AgiBot are both expected to list in the coming months.

Not everyone is convinced. Harold Soh of the National University of Singapore said home robots are still years away, with battery life, privacy, and reliability all unsolved. The near-term market is factories and hospitals. There’s also the Trump administration’s July announcement that it will ban new Chinese-made humanoid and quadruped robots on national security grounds, which Beijing called politicising trade. Robotics researcher David Hsu was blunter about where things stand: American robotics companies barely make the news anymore, and Chinese firms have taken their place as the cool kids on the block.

Read more here!

Ex-NVIDIA researchers raise over $90M for world model startup Veeda! 🌍

Sanja Fidler has left NVIDIA after eight years and started Veeda AI, a Toronto company building world models for physical AI. It has raised over US$90 million in seed financing from Radical Ventures and Khosla Ventures. That would make it one of the three largest seed rounds in Canadian history.

Sanja is the CEO, and her co-founders are both former NVIDIA colleagues: chief scientist Huan Ling, who worked with her at NVIDIA’s Toronto Spatial Intelligence Lab, and chief technology officer Zan Gojcic, a Zurich-based former research director. The three incorporated in early June and issued 60.6 million seed shares at US$1 each in late July, adding Radical partner Tomi Poutanen and Khosla partner Sven Strohband as directors on the same day.

Fidler described the company as building The Matrix for physical AI. The idea is that robot intelligence can interact with an entirely virtual world in a million possible ways and learn from its own mistakes, because trial and error in real life is neither safe nor economical. World models are typically trained on image and video data and simulate the consequences of actions, giving systems a better grasp of real-life mechanics. Veeda is hiring scientists and machine learning engineers, plus staff to collect training data and manage chip clusters. Its site lists offices in Toronto, Mountain View, Singapore, and Zurich, with a Swiss subsidiary set up in July.

Fidler is a significant figure in Canadian AI. She joined the University of Toronto’s computer science faculty in 2014, was a founding member of the Vector Institute, and holds a federal research chair. At Nvidia she helped build the company’s flagship world model alongside Ling, whose doctorate she supervised. She’s also the latest researcher to leave a US tech giant to start a foundation model company. Others in the world model space include Yann LeCun’s AMI Labs, Fei-Fei Li’s World Labs, and Waabi, led by fellow U of T professor Raquel Urtasun, which raised US$750 million in January co-led by Khosla.

Read more here!

Gravis Robotics raises $200M from SoftBank for autonomous construction! 🚜

SoftBank is putting $200 million into Gravis Robotics’ Series A, which the company says is the largest round in construction robotics history. Gravis spun out of ETH Zurich in 2022 and retrofits existing excavators and construction equipment with its Gravis Rack autonomous control kit and software.

The technical problem is that most physical AI works in static worlds. A self-driving car steers around obstacles on fixed pavement without disturbing anything. An excavator does the opposite: it deliberately reshapes the ground it’s working on. Gravis trains on simulated experience, moving billions of cubic yards of virtual material from soft clay to rock-filled soil. “Skilled operators read the earth through subtle physical feedback, listening to the engine strain, sensing the machine vibration and reacting to hydraulic resistance,” said co-founder and CTO Dominic Jud. “Our AI takes that same physical input and grounds it in machine telemetry, responding to varying subterranean forces and soil mechanics at microsecond speeds.”

The mixed-fleet angle matters commercially. Roughly two-thirds of global heavy equipment demand sits outside the top three manufacturers, and contractors pick machines based on regional service relationships and existing fleet investments. The Gravis Rack has been installed on machinery from Caterpillar, Case, Develon, John Deere, JCB, Hitachi, Sumitomo, Yanmar, and Volvo, and the same core software can drive a compact excavator or a machine five times its size without custom reprogramming. Gravis claims productivity gains of up to 30% over peak manual operations.

Operators can stay in the cab with Gravis Copilot, using real-time 3D guidance and hazard detection, or step away entirely and supervise robotic fleets when full autonomy is engaged. Either way, every machine with the kit acts as a mobile sensor, automating surveying and hazard mapping while other work happens. Gravis has deployed across four continents and was recently chosen to lead an $8 million UK government-backed CAM Pathfinder project with Flannery Plant Hire, the country’s largest equipment rental provider.

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Uber adds Zipline drones to its Eats delivery network! 🚁

Uber is investing in and partnering with Zipline, with the goal of making one million deliveries per day using the startup’s drones by the end of 2029. The first Zipline deliveries on Uber Eats will happen by the end of this year, starting in Zipline’s existing markets, with plans to expand into dozens of US cities. Neither company disclosed the investment amount.

Uber thinks Zipline’s drones can fulfill Eats orders within five to 10 minutes. “Truly quick commerce is proving to be an even bigger market than the original food market was,” CEO Dara Khosrowshahi told The Wall Street Journal. “We think this can be an enormous tailwind for the next leg of growth for Eats.”

This follows the playbook Uber has used for robotaxis: bring as many companies onto the platform as possible rather than building the technology in-house. The company sold off Uber Elevate and its Autonomous Technologies Group, then committed more than $10 billion to dozens of autonomous vehicle providers instead. It’s already working with Israeli drone startup Flytrex after a partnership announced late last year. The approach has limits, though. Uber recently clashed with Waymo, and the two are expected to part ways when their contracts expire in 2028, having also landed on opposite sides of a growing fight over autonomous vehicle regulation.

Zipline, based in San Francisco, recently closed an extended $800 million Series H that pushed its valuation to $7.6 billion. “Every great transportation revolution has changed where people live, how businesses operate, and how economies grow,” said co-founder Keller Cliffton. “Together with Uber, we’re taking the next step toward building a world where getting what you need is as fast and effortless as sending a text, no matter where you are.”

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Matic launches Cues, voice & gesture control for home robots! 🏠

Matic has launched Cues, adding voice and gesture control to its home cleaning robot. The pitch is simple: spill coffee, point at it, say “Hey Matic, clean this,” and the robot hears you, sees the gesture, locates the spot in 3D, and goes to clean it on its own.

The other commands work similarly. Say “Hey Matic” and it locates your voice, turns, and looks at you. Say “Hey Matic, follow me” and it walks behind you. Say “Hey Matic, go clean the living room” and it navigates there using the house map it already has. Matic says the system understands 75 languages and is simple enough for a five-year-old or an eighty-year-old to use.

On the cleaning side, Matic claims 8x the airflow of competitors, with specialised algorithms for rugs, corners, toekicks, and mopping, and says performance keeps improving through software updates. The company raised $115 million and spent nine years building the robot. Around 13,000 families use it, and WIRED gave it a 10/10, which the magazine hasn’t handed to a piece of hardware in a decade.

Matic is offering a full refund if buyers don’t love it after six months. To mark the launch, the company is cleaning 300 homes with Matic across San Francisco and New York City.

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Unitree prices IPO at $9B, first mainland-listed humanoid maker
→ 2,760x oversubscribed, 150.8 yuan/share raising $900M for R&D and manufacturing → Why humanoid sales (867.8M yuan) overtaking quadrupeds signals market maturity: real revenue, real profits, not speculation

Dyna Robotics’ Dyna-2 proves human-to-robot scaling law
→ 1M+ hours egocentric human video pre-training, zero-shot performs 39 robot tasks across embodiments
→ What monotonic scaling from 1K to 1M hours demonstrates: lockbox tasks unsolvable at 100K hours, 90% success at 1M

HII commits $900M to Path Robotics and GrayMatter over 7 years
→ Autonomous welding, sanding, grinding across Navy shipbuilding (carriers, subs, destroyers, unmanned vessels)
→ Why performance-based agreements with tech readiness gates signal procurement maturity vs R&D contracts

Nucleus Robotics deploys in operating factory in <90 days
→ Hardware-independent control software enabling multi-vendor humanoid coordination layer
→ Why intelligence layer (perception, planning, real-time adaptation, fleet coordination) matters more than locking to single vendor

Niantic/NVIDIA/Flexion compress months of adaptation to days
→ Photorealistic office scan enables RGB-only policy training with zero real-world fine-tuning
→ What real2sim photorealistic 3D Gaussian splats matching collision meshes reveal: visual and physics layers must agree or miscalibration transfers to hardware

Unitree prices IPO at $9 billion valuation in China! 🇨🇳

China’s Unitree has priced its Shanghai initial public offering at 150.8 yuan ($22.34) per share, valuing the company at around 61 billion yuan ($9.04 billion), in a deal that would make it the country’s first mainland-listed maker of humanoid robots. In a filing to the Shanghai Stock Exchange, Unitree, also known as Yushu Technology, said it was seeking to raise 6.1 billion yuan (roughly $900 million). The Hangzhou-based company is selling 40.45 million new shares, or 10% of its enlarged share capital, on Shanghai’s STAR Market, with Chinese AI company DeepSeek among the strategic investors. IPO subscriptions open August 10, with proceeds going toward robot software and hardware, new products, and building a manufacturing base.

The numbers in the prospectus are extraordinary for a hardware company. Overall revenue more than quadrupled to 1.7 billion yuan in 2025, with humanoid robots generating 867.8 million yuan in sales and overtaking four-legged robots as Unitree’s largest business. The company is profitable, and from 2023 to 2025 it sold 33,294 quadruped robots and 5,632 humanoids. Founded in 2016 by Wang Xingxing with cheap robot dogs, Unitree has completed an extraordinary 10-year arc to become the world’s first publicly listed humanoid robot manufacturer. Investor appetite has been staggering: the offering was reportedly oversubscribed by 2,760 times, with demand during the offline subscription process equivalent to 71.46 billion shares.

The same week as the IPO, Unitree quietly dropped a new low-cost modular humanoid starting at $4,290, with 31 degrees of freedom and flexible deployment. For context, Tesla’s Optimus is estimated at $20,000-$30,000. That pricing underscores how aggressively Chinese manufacturers are driving down the cost of humanoid hardware, even as the market runs hot on real revenue, real profits, and real products rather than pure speculation.

But the geopolitical backdrop is impossible to ignore. The IPO comes as the United States and China ratchet up trade and technology tensions, with Washington tightening Chinese access to U.S. technology and markets, including new restrictions on foreign-made humanoid and four-legged robots—and Beijing responding with export curbs and sanctions. U.S. sales accounted for 13.3% of Unitree’s revenue last year, and while its existing robots have U.S. approvals, future models could be barred from sale there entirely.

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Dyna Robotics releases a world-action model, Dyna-2! 📈

Dyna Robotics has introduced Dyna-2, a new flagship world-action model (WAM) pre-trained on more than one million hours of egocentric human video—roughly 170 years of continuous waking experience. The model establishes several novel scaling laws, most notably proving for the first time that a human-to-robot transfer scaling law exists: more human data in pre-training improves prediction on robot data the model has never seen, with zero robot data used during pre-training. The core bet is that the right source of pre-training data is sensorized video of humans performing everyday tasks, which already exists at effectively unbounded scale and carries exactly what a manipulation policy needs to learn, how scenes evolve, how objects respond to contact, and how hands interact with them.

Dyna-2 is a single generative model that can denoise future video and future actions jointly or separately, built on a video-diffusion backbone. Trained on a corpus of over one million hours (43.8 million clips, 97,160 unique task instructions, 9,917 distinct objects), the model shows that held-out prediction improves monotonically and follows a clean power law all the way to the million-hour scale. Crucially, that same trend transfers across the embodiment gap: evaluated zero-shot on 39 robot tasks across two embodiments, performance predictably improves as purely human data grows, with an inflection point emerging between 10k and 100k hours of pre-training.

The scaling law carries through to real robots in post-training. With just a few hours of robot data—and no robot data during pre-training—Dyna-2 performs tasks across bi-manual parallel-jaw arms, semi-humanoid, and dexterous hand platforms, with mean normalized performance rising from 20% to 53% as pre-training scales from 1k to 1M hours. Some tasks require a threshold amount of pre-training before becoming solvable at all: Lockbox Key Turning was never solved up to 100,000 hours, but at one million hours it succeeded 90% of the time. In one striking example, just 13 minutes of teleoperation data was enough to fine-tune the model to open bottle caps using two five-fingered robot hands.

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HII signs up to $900M agreement with Path Robotics and GrayMatter Robotics! ⚓

Huntington Ingalls Industries (HII) has announced long-term performance-based production agreements with GrayMatter Robotics and Path Robotics, intending to award up to $900 million in total shipbuilding work across seven years. Path Robotics provides robotic welding technology, while GrayMatter offers robotic sanding systems.

Both companies are part of HII’s High-Yield Production Robotics (HYPR) program, launched in April, which aims to accelerate advanced, adaptive automation in the fabrication of both crewed and uncrewed naval platforms. The funding is contingent on the two companies meeting clearly defined technology and manufacturing readiness, as well as performance milestones. With the agreement, HII plans to accelerate development and deployment of physical AI across U.S. Navy shipbuilding programs, including aircraft carriers, submarines, destroyers, amphibious ships, future frigates, and unmanned surface vessels.

The agreements include two stages. In the development stage, both companies will partner with HII to develop, validate, and qualify high-precision production techniques applicable across autonomous welding, grinding, blasting, painting, assembly, and inspection, then integrate them into an autonomous production line. For instance, Rove pairs Path Robotics’ Obsidian physical AI model with a quadruped robot to bring autonomous welding beyond fixed cells, deploying directly onto large assemblies and immovable structures.

In the delivery stage, HII will begin sourcing shipbuilding work from both companies through the new line, contingent on favorable cost, schedule, and quality performance, starting with small steel structures and growing to include units and modules. In 2026, HII plans to outsource more than 2.5 million hours of shipbuilding work, a 30% increase from 2025, while expanding its structural assembly network to enable more work to be completed outside the shipyards before final assembly.

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Robotics Maps - Odense 🇩🇰

Danish city of robotics! 🇩🇰

When I arrived in Odense in 2024, the first thing I saw was robots. Lots of them.

Cleaning robots greeted me at the station, an autonomous one trailing advertising banners rolled past, and a few minutes later I spotted UR robots playing tic-tac-toe in a shop window.

Odense is one of Europe’s strongest robotics ecosystems, especially for collaborative and industrial robots.

Nucleus Robotics exits stealth with 90-day factory deployment platform! 🥷🏻

Nucleus Robotics has emerged from stealth, with founder Melvin Schwarz announcing via X that the German company deployed humanoid robots inside an operating factory in under 90 days. The announcement serves as the first public test of Nucleus Robotics’ core pitch: software that helps humanoid robots adapt to existing industrial sites without rebuilding production lines.

Nucleus Robotics describes its core technology as an intelligence layer spanning perception, task planning, real-time adaptation, and fleet coordination for industrial humanoid operations. Registered in Gütersloh in February 2026, the company’s framework covers imitation-based training, real-time monitoring, and teleoperation capabilities for handling complex exception cases. By focusing on hardware-independent control software, Nucleus Robotics aims to enable manufacturers to add new robotic systems without locking factory workflows into a single hardware vendor.

Schwarz previously co-founded VisionAI before assembling a European robotics team with experience spanning major automation enterprises, research institutions, and aerospace organizations. The startup is now positioning its operator software layer to capture deployment management as manufacturers increasingly test humanoids from various global hardware suppliers, a bet that the future of industrial robotics will be multi-vendor, and that whoever controls the coordination layer will hold a valuable position in the stack.

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A humanoid learns to walk a real office by training entirely inside a photorealistic scan of it! 🏢

A collaboration between Niantic Spatial, NVIDIA, and Flexion Robotics has demonstrated something striking: a humanoid can learn to navigate a real office by training entirely inside a photorealistic scan of that office, then walk it with no real-world fine-tuning, using RGB camera vision instead of a depth sensor. The weight of the story is in the economics of deployment. Reinforcement learning is the scalable way to teach a humanoid to navigate, but a robot doesn’t get many second chances in the real world,a misjudged gap or a collision with a glass door is expensive and can damage hardware. So training happens in simulation, which raises the question: how do you know a policy will hold up at a specific site before the robot arrives?

Simulated worlds are usually randomized, untextured geometry, because depth is easy to simulate. But that leaves the robot blind to materials, semantics, and what things actually are. RGB is what humanoids already carry and encodes both geometry and meaning, but you can only train an RGB policy if the simulated imagery genuinely looks real. The unlock: if the training world is a faithful reconstruction of the actual deployment site, RGB becomes trainable—and the policy specializes in the building it will work in. Deploying to a new environment has traditionally taken months of on-site adaptation; this pipeline compresses that to days.

Each company owns a distinct piece. Niantic Spatial handles “real2sim”, turning a real place into a high-fidelity, simulatable copy from a few minutes of video off an off-the-shelf 360° camera, no LiDAR or specialist workflow. Crucially, the visual layer (a photorealistic 3D Gaussian splat) and the physics layer (the collision mesh) come from one reconstruction, so they agree by construction. Most pipelines build geometry separately and try to register the two—and if a rendered wall and a collision wall disagree by a few centimeters, the robot carries that miscalibration onto real hardware. NVIDIA handles training: the splat and mesh export as a single USDZ matching NVIDIA’s NuRec spec, loading directly into Isaac Sim and Isaac Lab, gravity-aligned and collider-ready with no manual conversion. Flexion handles “sim2real,” training an RGB-only navigation policy inside the reconstruction using domain randomization plus large-scale offline-trained image encoders that produce robust features on real imagery.

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Google DeepMind launches Gemini Robotics 2 for whole-body control
→ Vision-language-action model now controls full humanoids (feet to fingertips) vs previous upper-body table-top only
→ What same checkpoint controlling three different embodiments (Apollo 2 SharpaWave, Apollo 2 Inspire, Franka Duo Robotiq) reveals about generalization across platforms

Humanoid releases KinetIQ Ascend with real-world reinforcement learning
→ Why scaling trend suggests path to 100% reliability: robot performance improves predictably as training time added, similar to LLM compute scaling
→ Fleet learning loop: every deployed robot becomes part of continuous improvement system through supervisor interventions

Generative Bionics unveils Gene.01 with full-body tactile smart skin
→ Distributed multimodal sensing detecting touch, proximity, force, temperature enabling force-aware task teaching
→ Why physics-native AI optimizing hardware/mechanics/sensing together outperforms vision-only systems: infers physical forces for complex manipulation

Procore acquires DroneDeploy for $845M, creating construction AI
→ Combines 400M photos + 126M drawings from Procore with DroneDeploy’s 20 trillion sq ft visual data + 100K labeled safety issues
→ Why multi-modal perception (drones, ground robots, cameras) enabling autonomous action replaces manual jobsite inspections

Reimagine Robotics emerges from stealth with "learn on the job" robots
→ Google DeepMind's Scholz + colleagues launch London/Sydney startup reducing behavior prototyping from 1 day to 10 minutes
→ Why "monkey-see, monkey-do" approach (show, correct, repeat) eliminates specialist programmers vs fixed task programming

Gemini Robotics 2 brings whole body intelligence to robots! 🧠

Google DeepMind has introduced Gemini Robotics 2, the intelligence layer powering the next generation of adaptable robots. The major advance unlocks intelligent whole-body control, advanced dexterity, and multi-robot collaboration. Where previous models controlled only a humanoid’s upper body for table-top tasks, Gemini Robotics 2 can now control entire humanoid robots from feet to fingertips, enabling a robot to walk, crouch, stretch, and manipulate objects to clean up a cluttered room, and even team up with other robots to finish the job faster.

The release comes as three models. Gemini Robotics 2 is the most advanced vision-language-action model (VLA), converting vision and language input into motor control, capable of controlling full humanoids and other bi-arm robots. Gemini Robotics ER 2 is the most capable embodied reasoning model—a vision language model that acts as the robot’s high-level brain, enabling robots to communicate with humans, understand the physical world, and plan multi-step tasks lasting several minutes. Gemini Robotics On-Device 2 is the most efficient VLA, optimized to run locally on robotic devices with fast adaptation to new embodiments.

Gemini Robotics 2 unlocks a new level of physical dexterity across different end effectors. The model can control the five-fingered, 22 degree-of-freedom SharpaWave hand on the Apollo 2 robot to complete delicate actions like tying knots or sealing a ziplock bag, and can also operate standard two-fingered parallel grippers on a Franka Duo platform for complex tasks like tight packing. The same model checkpoint controls three different embodiments, the Apptronik Apollo 2 with SharpaWave hands, Apollo 2 with Inspire hands, and the Franka Duo with a Robotiq gripper, though DeepMind notes multi-finger dexterous manipulation remains challenging.

Safety remains foundational to the research. DeepMind is introducing ASIMOV-Agentic, a new benchmark for agentic safety orchestration and uncertainty resolution that measures the reasoning agent’s ability to refuse unsafe tool calls and proactively request human intervention when uncertain. With enhanced embodied reasoning, Gemini Robotics ER 2 is the company’s safest robotics model to date in safety constraint following and human proximity benchmarks, better detecting when humans are nearby and bringing the robot to a safe stop if someone approaches too closely.

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Humanoid released its new platform KinetIQ Ascend 🦿

Humanoid has revealed how it uses reinforcement learning (RL) to improve its robots’ performance through experience, rather than relying solely on human demonstrations. Currently, imitation learning is the primary way to teach robots manipulation skills, an approach that works well for impressive demos but doesn’t create robots that can outperform people or operate reliably in real-world environments.

The company’s new approach, KinetIQ Ascend, extends KinetIQ, the proprietary AI framework behind its robots, with the ability to learn from trial and error. Humanoid believes this is the first published demonstration of end-to-end, vision-based RL running on production humanoid manipulation systems in real-world deployment conditions.

One of the key findings is that robot performance improves in a predictable way as more robot training time is added, similar to how large language models improve as more compute and data become available. The observed scaling trend, supported by simulation experiments, suggests the method scales all the way to 100% reliability. The results were demonstrated on several industrial tasks. In a machine-feeding application where a robot picks steel bearing rings from a bin and places them onto a conveyor, RL increased throughput by 42%, with the robot performing at 1.5x the speed of the human demos it originally learned from. In a second task picking items from a cluttered tote and handing them to a person, throughput increased by 85% and success rates improved from 80% to 98%.

The robots achieved these improvements after only a few days of learning. The company also uncovered two findings that exceeded expectations. First, improving only the most difficult part of a workflow can improve the performance of the entire task. Second, the robots appeared to generalize what they learned. After training on a single object type, performance improved on other objects the robots had never seen during training. Humanoid describes its approach as building a “capability factory,” a system designed to rapidly create, improve, and scale robot skills across industrial environments.

The long-term vision goes even further. Because the same training framework can run on deployed robots, every robot could eventually become part of the learning system, meaning fleets would continuously improve in a feedback loop: better robots generate better data, leading to even better robots over time. When a supervisor steps in to correct or assist a robot on-site, that intervention automatically becomes a training signal—turning routine operation into continuous improvement without any additional data collection effort. The humanoid race is becoming a question of scale, and Humanoid’s research suggests real-world RL may be one of the technologies that helps robots perform thousands of tasks every day with speed, reliability, and consistency.

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Italian humanoid with smart skin for safe collaboration! 🫆

Italian deep-tech company Generative Bionics has unveiled significant updates to Gene.01, a humanoid robot designed for industrial applications. Developed in just six months, the robot combines full-body tactile sensing with a physics-native AI system that integrates its body, mechanics, and intelligence to better perceive, interpret, and respond to the physical world.

Unlike conventional humanoids that primarily rely on vision, Gene.01 features a distributed multimodal “smart skin” covering its body. The tactile system detects touch, proximity, force, and temperature, allowing the robot to sense people before and during physical contact, enabling more natural interactions while improving safety in shared workspaces. The force-sensing capability also allows workers to teach tasks by physically guiding the robot and demonstrating the required force for operations such as gripping or handling objects. This addresses limitations of vision-only learning systems, which often struggle to infer the physical forces needed for complex manipulation.

At the core of the platform is Generative Bionics’ proprietary physics-native AI, which jointly optimizes the robot’s hardware, mechanics, sensing, and motion intelligence rather than treating them as separate systems. The AI uses digital models of both the robot and its human partner to optimize movement, ergonomics, balance, and walking stability. The architecture is based on research published in Nature Machine Intelligence describing a human-aware embodied intelligence framework originally developed through the ergoCub humanoid project.

Generative Bionics is also releasing Gene.01’s digital twin as open source, making it available through standard software distribution ecosystems including PyPI, conda-forge, and the official ROS build farm, giving Physical AI developers a common foundation for simulation and testing before field deployment. Rather than marketing Gene.01 as a one-size-fits-all humanoid, the company has designed it as a scalable platform customizable for manufacturing, logistics, inspection, healthcare, and public safety. Its first industrial deployment is being developed with Italian shipbuilder Fincantieri, where Gene.01 is being adapted into a welding humanoid for shipyard operations, while a partnership with German actuator specialist Synapticon will build out a European supply chain for future production.

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Robotics Maps - Austin 🤠

Austin has quickly become one of the fastest-growing robotics hubs in the US.

Thanks to its mix of AI talent, advanced manufacturing, and a strong startup culture.

The ecosystem is supported by The University of Texas at Austin, which produces top engineers in robotics, AI, and computer science. Around the university, a growing number of startups are developing autonomous systems, drones, and humanoid robots.

Austin has also attracted major technology companies. Tesla operates its Gigafactory near the city and is developing the Optimus humanoid robot, creating a large pool of robotics, AI, manufacturing, and automation talent.

Procore Technologies acquires DroneDeploy for $845M! 🏗️

Procore Technologies, a construction management software provider, has announced it will acquire DroneDeploy for approximately $845 million in cash. DroneDeploy offers a robotics and visual intelligence platform for the built world that has been used on over 3 million job sites across more than 180 countries.

DroneDeploy’s visual intelligence technology bridges the physical world—including active construction sites and operational assets, with the digital world, providing real-time visibility into daily operations. The company provides perception through three-dimensional ground and aerial imaging, spanning drones, ground-deployed robots, and mobile, fixed, and wearable cameras. When combined with Procore AI, that captured visual data can be translated into observations that drive autonomous action within the Procore platform.

The companies said the combination of their technologies will enable users to see, understand, and act within Procore, replacing manual jobsite inspections with multi-modal perception capabilities. A range of cameras, drones, and robots can regularly evaluate construction sites and automatically initiate appropriate responses.

Once completed, the transaction will bring together two extensive, domain-specific datasets: Procore’s record of construction decision-making, encompassing nearly 400 million photos, over 126 million drawings, and over 10 million RFIs, submittals, and inspections in the past year alone—and DroneDeploy’s visual record of physical builds, spanning approximately 20 trillion square feet of visual data, tens of millions of user-generated annotations, and more than 100,000 labeled safety issues. Procore plans to integrate capabilities including real-time perception and advanced reasoning to deliver “digital co-workers” for the field and back office. The transaction is expected to close later this year, subject to customary closing conditions and regulatory approvals.

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Reimagine Robotics emerges from stealth with robots that ‘learn on the job’! 🐒

Reimagine Robotics has emerged from stealth, developing technology it says anyone can train and use, enabling robots to learn on the job. “A useful robot should be able to learn from the person doing the work,” stated Jonathan Scholz, co-founder and CEO. “They should be able to show it a task, put it right when it makes a mistake, and move on to the next problem. That is what it means for a robot to learn on the job. It’s a process we call ‘monkey-see, monkey-do.’” Scholz founded Google DeepMind’s Applied Robotics team in London and led it for seven years, later co-founding Reimagine Robotics in April 2025 alongside former colleagues Oleg Sushkov, Akhil Raju, and Misha Denil, with headquarters in London and Sydney.

Instead of requiring specialist programmers whenever a task or production process changes, Reimagine Robotics lets workers show the robot what to do, watch it attempt the task, and then correct it on the spot. “A robot should arrive with the attitude of a new colleague: ‘How can I help? What do you want me to do?’” said Scholz. People remain central to the model throughout deployments. “For us, this is not about taking people out of the process. A robot that learns on the job depends on people. The worker identifies the bottleneck, shows the robot how to help, and corrects it until it is useful. I think of it more as a tool to amplify human labor.”

The company’s first phase was funded by pre-seed financing from Fly Ventures, firstminute capital, and a number of angel investors, and it has already deployed its robots in advanced manufacturing and electronics disassembly facilities. At a made-to-order plastics business, Reimagine Robotics trained its robots to tend 3D printers overnight by removing print beds, operating latches, and pressing controls—and the customer’s own team used the platform to automate additional stages including washing, curing, and drying. In a separate deployment recovering valuable critical materials from used hard drives, the company worked with process engineers to develop a three-robot disassembly cell combining robots and people to optimize the workflow in real time.

During that project, Reimagine Robotics said it reduced the time required to prototype and test a new robot behavior from approximately one day to about 10 minutes, allowing teams to propose a new use for a robot and test their ideas almost immediately. “This last year was about building a core product and a team, and working with customers to test the platform,” said Scholz.

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Humanoid raises $152M Series A at $1.35B valuation
→ Why Schaeffler partnership involving thousands of robot deployments signals commercial validation beyond funding
→ Q4 beta deployments across manufacturing, logistics, retail with Bosch as contract manufacturer

Walden Robotics launches from stealth at $1.1B valuation
→ Toyota and Deviation Capital co-lead $300M, general-purpose robots in production at Toyota plant since February
→ What Diffusion Policy and Large Behavior Models (LBMs) demonstrate: continuous learning through real-world practice

mimic robotics unveils M1 hand and U1 wearable exoskeleton
→ 15 active DOF matched to human hand, tendon-driven, 25kg payload, <0.05 Nm backdrive torque enabling 50g weight sensing
→ Why keeping morphology constant across learning pipeline matters: no cross-embodiment gap between human demos and robot execution
→ Data pyramid approach: video (base) → wearable (middle) → robot teleoperation (top quality)

Sunday Robotics previews ACT-2, achieves 99.1% zero-shot laundry success
→ 785 autonomous attempts across 9 garment types, unseen homes, no per-home adaptation using single fixed model
→ What single-demonstration learning generalizing to unseen environments reveals: post-training gains transfer to real-world performance

Humanoid raises $152 million Series A at $1.35 billion valuation! 💰

UK-based robotics startup Humanoid has raised $152 million in a Series A funding round at a post-money valuation of $1.35 billion, as investors pour money into companies developing robots that can augment human labor. The round was led by Prime Movers Lab, with participation from Schaeffler, Bosch, Fubon Financial Holding Venture Capital, and Aglaé Ventures, bringing Humanoid’s total funding to $270 million.

The proceeds will fund development of the company’s next-generation platform, expansion of its proprietary AI software platform KinetIQ, commercial deployments with industrial customers, and the start of mass manufacturing of wheeled robots. Humanoid plans to begin deploying beta robots at customer sites in the fourth quarter of this year across manufacturing, logistics, retail, and other industrial sectors.

The company said it has partnerships with SAP, Nvidia, Bosch, and Siemens, and recently signed an agreement with Schaeffler involving the planned deployment of thousands of robots at manufacturing facilities. Bosch said it would serve as Humanoid’s contract-manufacturing partner while providing technical expertise in hardware design, production, and supply chain operations.

The financing signals growing investor appetite for “physical AI” companies that combine artificial intelligence with robotics, as manufacturers and logistics companies increasingly look to humanoid robots to address labor shortages and automate repetitive industrial tasks.

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Walden Robotics launches with $300 million to put general-purpose robots to work today! ⚙️

Walden Robotics, a full-stack Physical AI company building and deploying general-purpose robots that continuously learn and improve while performing real work, has launched out of stealth with $300 million in funding at a $1.1 billion valuation. The round is co-led by Toyota (Toyota Motor Corp, Toyota Invention Partners, and Toyota Ventures) and Deviation Capital, with participation from NVIDIA, Boeing, AE Ventures, Samsung Ventures, Prologis Ventures, CoreWeave Ventures, and more.

The Cambridge, Massachusetts-based company deploys robots into real production environments in manufacturing and logistics, doing useful work side-by-side with people from day one. Walden enables skilled team members to delegate difficult-to-automate tasks so they can focus on problem-solving, judgment, and craft. Its robots are useful across industries, with strategic partners in automotive, aerospace, semiconductors, electronics, logistics, and life sciences, meeting fast-growing needs from industries facing labor shortages, demographic shifts, competitive pressure, and increased demand.

Walden’s product builds on a decade of foundational AI and robotics research the team helped pioneer, including Diffusion Policy and Large Behavior Models (LBMs), the frontier model class that powers Walden’s robots and lets them quickly learn new tasks and continuously improve through real-world practice. The company launched out of Toyota Research Institute in January 2026 and began working with customers across multiple industries immediately. Since February, Walden’s general-purpose robots have been doing useful work in production at a Toyota plant in North America, moving from first pilot to real work in under two months.

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mimic robotics unveils their hand M1 and wearable U1! 🤌🏼

Swiss company mimic robotics has announced the mimic hand M1, a highly backdrivable, tendon-driven robotic hand designed and built in-house for industrial automation. Alongside it comes the mimic wearable U1 (”umimic”), an exoskeleton that captures human demonstrations directly matched to the kinematics of the M1. Together with a fully in-house software stack, the systems form an integrated platform for general-purpose dexterous manipulation.

The core bet is keeping morphology constant across every phase of learning by running the entire pipeline on hands. Traditional two-finger grippers create a cross-embodiment gap: if you pre-train on human video and then deploy a gripper, the end effector is different and the steps a human takes to manipulate an object aren’t the steps a gripper needs. mimic never introduces that gap. The company organizes its data around a “data pyramid”, human video at the base (lower quality, massive scale), wearable device data in the middle, and robot teleoperation and deployment data at the top (highest quality, collected on its own robots).

The M1 has 15 active degrees of freedom across 21 joints, matched to the most functional degrees of freedom of the human hand including abduction and an opposable thumb. It delivers over 25 kg steady-state payload in a cylindrical power grasp, 25 N fingertip force, backdrive torque under 0.05 Nm (low enough to sense weights as light as 50 g through motor current), force estimation sensitivity under 0.1 N, and fingertip position accuracy of ±0.18 mm. Rather than packing motors into the palm, mimic relocates actuators to the forearm and drives the fingers through tendons routed over bearings and pulleys, replacing sliding friction with rolling friction for predictable, consistent behavior across units and cycle counts — essential for learned policies to transfer between hands.

The U1 wearable matches both the kinematics and contact geometry of the M1, with a rigid linkage mechanism enforcing 1:1 correspondence on every degree of freedom. Tactile sensors, joint encoders, and wrist cameras are placed in one-to-one correspondence with the robot, so demonstrators experience the same sensing modalities the robot will. The tradeoff: geometry is fixed to the robot rather than adapting to every user’s hand, but in return demonstrators feel contact forces through their own fingers and work at nearly natural human speed without control latency or retargeting.

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Experiencing the Sound of Science live at Siemens Transform UK! 🎼

I had the chance to attend Siemens Transform UK in Manchester this week, and it was amazing to experience Siemens’ Sound of Science acoustic digital twin live, meeting Cedrik Neike and Stephan Frucht in person and hearing, quite literally, what the future of acoustic simulation sounds like.

For the first time in the UK, Siemens brought its Sound of Science application to a British venue: Hallé St Peter’s in Manchester, home to the Hallé Orchestra’s rehearsals and recordings. What makes this special is that Siemens has built a full acoustic digital twin of the space. It draws on the building’s layout and architecture, combined with a dry studio recording of a musical piece, to create a life-like, physics-based simulation of what a concert will sound like before a single note is played in the real room. Standing there and hearing a performance “rendered” through the actual acoustics of the hall, before any rehearsal, was genuinely striking.

Conductors and venue operators can explore different orchestra layouts and evaluate their acoustic impact without staging full rehearsals, cutting rehearsal times and logistics costs. The application even lets you experience how sound varies throughout the venue, and can transport you back to the building’s original church acoustics through an immersive VR experience. Hallé St Peter’s, a former church in the heart of Ancoats, was a fitting choice: it represents the “church” category among the four key venue types Siemens works with (concert halls, churches, opera houses, and festival halls).

What I found most compelling is the bigger story behind it. This is the same digital twin technology Siemens deploys across industry, modeling products, factories, buildings, and processes virtually before making changes in the physical world, applied here to a cultural setting. And Siemens’ connection to music runs deep: Ernst von Siemens acquired Deutsche Grammophon and built it into the world’s leading classical label, and the company remains closely tied to the arts through the Siemens Arts Program, which empowers young people in the arts, not just engineering.

Talking with Stephan Frucht, artistic director of the Siemens Arts Program, that “arts + tech” bridge really came through. As he put it, the digital twin technologies already helping industry test ideas and optimize resources now bring those same advantages to the concert hall.

Technology changes fast, but when you can anchor it to something as beautiful and human as music, it becomes something people can trust and understand, and that’s exactly what made experiencing it live so memorable.

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Sunday previews ACT-2, hitting 99% zero-shot success! 👕

Sunday Robotics has previewed ACT-2, which it calls the first robotics model to achieve reliability by unifying broad generalization with high performance. The headline result: 99.1% zero-shot success on laundry folding across diverse, unseen home environments, measured across 785 autonomous attempts spanning 9 garment types. ACT-2 builds on ACT-1, introduced in November 2025, which demonstrated long-horizon mobile manipulation and generalization to unseen homes. Where ACT-1 expanded what robot foundation models could do, ACT-2 focuses on how reliably those capabilities hold as conditions change.

The central breakthrough is that reliability gains from rapid post-training on limited in-house data generalize to real, unseen homes. Sunday found that scaling pretraining sharply narrows the generalization gap between in-domain and out-of-domain performance, meaning in-house improvements become a reliable predictor of real-world performance. The company also reports being the first to demonstrate that an end-to-end model can learn a new behavior from a single demonstration and generalize it to unseen environments, they post-trained four copies of the same pretrained model, each on one demonstration of a different folding technique, and all four successfully executed their new techniques on a held-out garment.

The evaluation covers T-shirts, long-sleeved tops, polos, sleeveless tops, blouses, pants, leggings, and shorts across sizes from XXS to 8XL, with garments starting in baskets, piles, on beds, or on the ground, arbitrarily oriented and naturally crumpled, all with zero per-home adaptation and the same fixed model checkpoint. Fold quality averaged 4.72 out of 5 stars across 778 completed folds, with 98.3% rated four stars or better and 73.8% receiving a perfect score. Median completion time was 2 minutes 13 seconds. Emergent behaviors included retrieving clothes from the floor, replanning under disturbance from children and adversarial interference, and repositioning its mobile body to handle everything from baby clothes to 53-inch towels.

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1X equips NEO with human-level dexterous hands
→ 25 actuated DOF, 5-to-15:1 gear ratios, tendon-driven enabling force transparency and tactile sensing
→ What backdrivable joints revealing grip pressure, contact location, shear demonstrates: hands simultaneously motors and sensors

LimX Dynamics prepares IPO at $2.21B valuation
→ Chinese humanoid startups rushing public listings as investment hits $6.95B in Q2 alone (6x YoY)
→ Why “0 to 1” innovation crossed, next barrier is product-market fit not technology → 100+ Chinese humanoid companies competing, Unitree fast-tracked Shanghai listing, 50K humanoid shipments forecast

Holiday Robotics raises $105M Series A, largest Korean humanoid round
→ Friday robot built around 64 DOF (40 in hands) with tactile sensing, force-aware control, wheel-based mobility
→ What Vision-Language-Skill (VLS) stack reveals: vision/language define goals, reusable skills execute with full deployment infrastructure in-house

NVIDIA and Hugging Face integrate Isaac GR00T 1.7 into LeRobot
→ Open reasoning VLA model + Isaac Teleop framework for human demonstrations now in open source library
→ What 350K trajectories, 57M grasps, 15M dataset downloads demonstrate about democratizing robot development

Human-level hands? 1X just gave humanoid robot NEO something close! 🪬

1X has released the hands for its NEO humanoid robot, showcasing unprecedented speed and precision. The headline spec: 25 degrees of freedom, all of them actuated, and tendon-driven motion, resulting in what 1X calls near human-level dexterity, strength, and reliability. The hands have enough strength to lift a 20-pound kettlebell yet enough delicacy to pick grapes off their stems, install a light bulb, and pick up a screw off the floor.

Most robot hands are essentially “write-only”, you command the hand to move to a position, it goes there, and no data comes back. It doesn’t feel anything the way a human feels things, thanks to aggressive gearing (often 100-to-1 or 200-to-1) that swallows any contact force feedback before it reaches the motor. Neo’s hand runs at dramatically lower gear ratios, roughly 5-to-1 to 15-to-1, using tendons to pull the fingers. The result is that every single joint is simultaneously a motor and a sensor—push on a finger and it gives (it’s backdrivable), and it reports exactly how hard you pushed. 1X calls this “force transparency”: force flows out, information flows back, and the robot knows what’s happening at its fingertips.

There’s also a skin layer doing real work: high-resolution tactile sensing across the fingertips that measures pressure, contact location, and shear, the sideways force that tells the hand something is about to slip. So when an object starts sliding out of its grip, Neo can theoretically feel it and re-grip in real time, just like humans.

Uniquely, these hands are washabl, IP68 sealed, just like a phone, so Neo can immerse them in water without any problems. That means Neo can fry an egg or chop vegetables and then clean up after itself, rather than spreading mess around the house. 1X says the hands have been through millions of test cycles, with wrist joints proven past two million cycles under load.

As 1X points out, while legs are for getting a robot somewhere and the body houses the battery and compute, the hands are what do the actual work—folding clothes, putting away laundry, cooking, whatever the chore is. Notably, Neo will ship with more hardware capability than the AI currently knows how to use, meaning over-the-air updates should make each Neo progressively smarter over time.

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Chinese humanoid startups are rushing to launch IPOs! 📊

Humanoid startup LimX Dynamics is getting ready to go public, just over four years after it was founded during the pandemic. “Listing is a must,” said founder Will Zhang, emphasizing the importance of timing, ahead of the company’s announcement that it had raised $200 million in a pre-IPO round valuing the startup at 15 billion yuan ($2.21 billion). Several overseas investors participated, including UAE-based Stone Venture, Italy-based GGG, and Germany-based Redstone VC. The startup said it is already preparing for its IPO, likely in Hong Kong, and is in a confidential phase of review.

Zhang compared the situation to Chinese electric car startups Nio, Xpeng, and Li Auto, which successively listed in the U.S. from 2018 to 2020. “Once the technology is mature, if [the company] doesn’t list, then like WM Motor, it may disappear,” he said. The urgency comes as China now has well over 100 humanoid companies, which fall under the national push for “embodied AI.” Reflecting a rapid surge in interest, investment in the sector hit 47.09 billion yuan ($6.95 billion) in the second quarter, more than double that of the first quarter, and up over six times versus the same period last year, according to industry data provider Xiniu.

China has fast-tracked approval for humanoid company Unitree to list in Shanghai, while Hong Kong processes applications from more than 500 companies across sectors. “With more industrial and collaborative robot companies potentially coming to IPO, competitive pressure is likely to persist,” Morgan Stanley said in a report, noting sector players DeepRobot and Leju that are looking to list soon. The investment firm forecasts 18% growth in China’s industrial robots market this year, and shipment of 50,000 humanoids.

LimX aims to create fully autonomous commercial service robots. The company said it will kick off a multi-year plan to ship thousands of humanoids to the Middle East, and is delivering its entertainment-focused Luna humanoid to customers in South Korea. To Zhang, the technology behind humanoid robots has already crossed the “0 to 1” line of innovating from scratch—the next barrier to entry lies in making a good product that meets users’ needs. Other backers in the latest round include Chinese precision parts company Lens Technology, IDG Capital, WestSummit Capital, Nio Capital, and Hefei Binhu Industry Development Group.

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Holiday Robotics raises $105M in Series A funding! 🇰🇷

South Korea’s Holiday Robotics has raised $105 million in Series A funding to develop dexterous humanoid robots for industrial work. South Korean business newspaper Maeil Business reported that the round is the largest Series A investment for a South Korean startup and the largest single funding round for a South Korean humanoid robot company. Existing investors Stone Bridge Ventures, Atnum Investments, Intervest, and Spring Camp participated, alongside new backers including IMM Investment, SL Investment, KB Investment, Bon Angels Venture Partners, and Goodwater Capital. Financial institutions including Korea Development Bank and Industrial Bank of Korea also participated.

Founded in 2024, the company will use the capital to grow its research and engineering teams, strengthen manufacturing readiness, and continue developing Friday, its humanoid robot. The company’s approach starts with hands. Holiday said humanoid robots create industrial value through grasping, placing, adjusting, sensing contact, and repeating precise movements, tasks difficult to automate because they involve friction, weight, alignment, and surface variation. Friday has 64 degrees of freedom, with 40 dedicated to its hands. The robot is built around high-dexterity hands, tactile sensing across the hand, and force-aware control that allows it to feel contact and adapt to it. It uses a wheel-based mobile base to move through factories, a dexterous upper body for manipulation, and hot-swappable batteries to support full-shift operation. Safety is built into the hardware, with lightweight hands, fully backdrivable hand and arm joints, and 360-degree perception using multiple cameras and LiDARs.

Holiday is also developing its software and deployment stack in-house, built around what the company calls Vision-Language-Skill (VLS). In that approach, vision and language help define what a robot should do, while reusable skills define how the robot performs the task. The full-stack system includes Friday, Holiday Sim (a simulation environment), Holiday Lab (a system for turning demonstrations and failures into reusable skills), and OASys (a deployment system).

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Robotics Maps - The Bay Area 🌁

This is the capital of robotics! 🌁

Silicon Valley is home to so many physical AI companies that you could spend a month visiting them and still not see even 10% of them (trust me, I tried).

So, the Bay Area is the world's leading ecosystem for robotics startups, bringing together top AI talent, top universities, experienced founders, and unmatched access to capital.

The region is anchored by Stanford University and University of California, Berkeley, two of the world's top universities for AI and robotics. They produce a constant stream of researchers, founders, and breakthrough technologies.

Check my LinkedIn post with all companies listed: CLICK HERE

NVIDIA and Hugging Face bring new models and frameworks for the open robotics community! 🔓

NVIDIA and Hugging Face are collaborating to bring the NVIDIA Isaac GR00T 1.7 open reasoning vision-language-action (VLA) model for humanoid robots and the NVIDIA Isaac Teleop framework to LeRobot—Hugging Face’s open source library for robotics, with NVIDIA Cosmos 3, a frontier model for physical AI, planned soon. Together, these integrations give developers a more accessible and standardized path for end-to-end robot development while driving innovation and collaboration across the open robotics community.

The integrations include NVIDIA Isaac Teleop (an open source framework for robot data collection that captures high-quality human demonstrations), NVIDIA Isaac GR00T 1.7 (the first open and commercially viable robot foundation model), and the upcoming NVIDIA Cosmos 3 (a frontier world foundation model that will help developers generate and augment robotics data and simulate scenarios when real-world data is limited).

These integrations build on a broader set of NVIDIA resources already connected to LeRobot. That includes the largest open source physical AI dataset—downloaded more than 15 million times—which features more than 350,000 real and simulated trajectories and 57 million grasps. It also includes NVIDIA Isaac Sim– and Isaac Lab-based simulation frameworks to help developers set up environments, generate robot data, and validate behaviors before moving to physical robots, plus NVIDIA Isaac Lab-Arena in the LeRobot Environment Hub for prototyping complex simulation environments.

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Weave Robotics launches Isaac 1 mobile home robot
→ Why hybrid autonomy (autonomous + remote ops for edge cases) matters more than full autonomy claims
→ $7,999 or $449/month, California deliveries fall 2026, expanding nationwide 2027

Agility Robotics goes public via SPAC at $2.5B valuation
→ Digit deployed across 9 customer sites (Schaeffler, GXO, Toyota, Mercado Libre) with $300M+ in multi-year orders
→ Why $620M raised for production scaling + 30-customer pipeline signals transition from pilots to commercial deployment

Apptronik launches Apollo 2 in bipedal and wheeled configs
→ Expanded 90K sq-ft Robot Park in Austin generates high-quality training data for Google DeepMind’s Gemini Robotics foundation models
→ What modular configurations reveal: bipedal for adaptability, wheeled for safety compliance in existing operations

Flexion’s Reflect v1.0 achieves 90% success on complex missions
→ Custom VLM mission controller continuously monitors, reasons, and replans across multi-step tasks vs 38% without RL
→ Why natural-language instructions enabling real-time prompt changes matter more than task-specific programming

Weave Robotics is ready for house chores! 🧺

US-based Weave Robotics has released Isaac 1, a wheeled mobile home robot designed to automate everyday household chores such as tidying rooms and doing laundry. The robot can make beds, sort clutter, fold clothes, and put items away while operating autonomously in most situations. When it encounters tasks it cannot complete independently, remote human assistance steps in to ensure the job is finished. Isaac 1 succeeds the company’s earlier Isaac 0, a stationary autonomous robot launched in February 2026 that folds laundry in 30-90 minutes.

Unlike its predecessor, which remained stationary at a table, Isaac 1 features a motorized wheeled base that allows it to move throughout a home. The robot is equipped with a telescopic body that adjusts its height from about 3 feet (0.9 m) to 5 feet 9 inches (1.75 m), enabling it to reach furniture, beds, shelves, and laundry baskets while maintaining a compact profile when idle. Its upper body houses two robotic arms designed for household manipulation tasks, using multiple onboard cameras and AI-powered perception to identify objects, understand room layouts, and determine where misplaced items belong.

The robot operates autonomously by default but can transition to remote human teleoperation whenever it encounters situations beyond its current capabilities. Through this hybrid autonomy model, a remote operator can temporarily guide the robot using its onboard camera feeds to complete difficult tasks before returning control to the autonomous system. To address privacy concerns, the company says Isaac 1 includes hardware controls that physically disable its cameras when they are not required for operation.

Weave Robotics plans to continue expanding Isaac 1’s capabilities through over-the-air firmware updates, allowing the robot to perform additional household tasks as its AI models improve. The company is offering the robot for $7,999 or through a $449-per-month subscription. Customers can reserve a unit with a $250 deposit, with deliveries scheduled to begin in California in fall 2026 before expanding across the United States during 2027.

Read more here!

Agility Robotics to go public via SPAC in a $2.5B deal! 📈

Agility Robotics, the humanoid robotics startup that spun out of Oregon State University in 2015, plans to go public through a merger with special purpose acquisition company Churchill Capital Corp XI in a deal that values the company at roughly $2.5 billion. The transaction is expected to generate more than $620 million in proceeds, including about $200 million from a group of new and existing institutional investors.

Agility is best known for Digit, a bipedal robot that is being used across nine customer sites, including with Schaeffler, GXO, Toyota Motor Manufacturing Canada, and Mercado Libre. The company has enjoyed backing from high-profile tech companies and funds like Amazon, Nvidia, SoftBank Vision Fund 2, and DCVC. Now it plans to use the capital raised in the SPAC merger to increase production capacity of its next-generation Digit v5, fulfill existing orders, and expand to new and existing customers.

The company said it has secured more than $300 million in multi-year orders for the new model and a pipeline of more than 30 potential customers evaluating large-scale deployments. “Humanoid robots are poised to become a critical driver of productivity, supply chain resilience, and American technology leadership,” Agility CEO Peggy Johnson said. “With commercially deployed humanoids already operating in customer environments today, Agility is helping enterprises address labor shortages, improve efficiency, and safely integrate AI-powered automation into their operations.”

The combined company is expected to trade under the ticker symbol AGLT on a North American stock exchange that has not yet been announced. The move makes Agility one of the first pure-play humanoid robotics companies to pursue a public listing, marking a significant milestone as the industry shifts from pilot deployments toward commercial scale.

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Apptronik releases Apollo 2, and a data collection and training facility! 🦿

Apptronik has made two major announcements. First, the company launched Apollo 2, its updated humanoid robot. Second, it opened its newly expanded Robot Park, its flagship data collection and training facility for humanoid robots in Austin, Texas. Apollo 2 comes in both bipedal and wheeled-base configurations, designed to learn real-world work through large-scale data collection across a wide range of tasks and environments. As part of Apptronik’s research partnership with Google DeepMind, the data Apollo 2 collects helps advance Gemini Robotics, Google DeepMind’s foundation models for robotics.

Their Robot Park enables the data collection that is fuel for that, and Apollo 2 is the machine that makes it possible.” Apollo is based on nearly a decade of development on 15 previous robots, including NASA’s Valkyrie. Apptronik started out of the Human Centered Robotics Lab at the University of Texas at Austin, has nearly 300 employees, and earlier this year raised $520 million, bringing total capital raised to nearly $1 billion.

By offering Apollo in modular configurations, Apptronik said it can optimize data collection across a variety of operational environments. The wheeled-base is designed to conform with existing safety standards for industrial mobile robots, allowing it to fit easily into existing customer operations, while the bipedal configuration provides maximum adaptability for complex environments. Everything the company is proving through the Apollo 2 platform is directly powering the development of its commercial product, Apollo 3.

Inside the newly expanded nearly 90,000-sq.-ft. facility in Austin, both bipedal and wheeled Apollo 2 systems learn across an array of customer use cases in logistics, manufacturing, retail, and other activities. Through a combination of teleoperation and autonomous execution, Apollo 2 continuously generates significant quantities of high-quality training data used to train and refine the Gemini Robotics AI models.

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Robotics Maps - Massachusetts 🦞

The Massachusetts ecosystem is one of the world's leading hubs for robotics, combining top research, companies, and deep-tech investment.

At its core are Massachusetts Institute of Technology (MIT) and Harvard University, which produce world-class researchers, engineers, and robotics startups.

Many of today's leading robotics companies trace their roots back to these universities.

The region is also home to companies like Boston Dynamics, whose work has inspired advances in humanoid and mobile robots.

Flexion’s new model gives humanoid robots 90 percent success in complex missions! 🎯

Flexion Robotics has introduced Reflect v1.0, a robotics intelligence platform that enables humanoid robots to complete complex, multi-step tasks autonomously, without human intervention during execution. The company showcased the system through a workplace delivery demonstration in which a humanoid robot retrieved a snack parcel, navigated stairs and an elevator, unpacked the box, and stored the items in a drawer, all from a single natural-language instruction. Reflect v1.0 unifies reasoning, perception, motion, control, and runtime into one robotics platform.

Long-horizon autonomy remains one of the biggest challenges in robotics because even highly reliable navigation, manipulation, and perception systems can collectively fail when errors accumulate across many sequential tasks. Reflect v1.0 addresses this by integrating a custom vision-language model (VLM) that acts as a mission controller, continuously monitoring progress, reasoning about the environment, and replanning whenever necessary. The platform combines this mission controller with a motion layer that uses vision-language-action models trained on real-world data alongside reinforcement learning-based skills, while a real-time whole-body controller maintains balance, stability, and precise movements throughout the mission.

A key feature of Reflect v1.0 is its use of natural-language instructions rather than task-specific programming. Users can modify missions simply by changing the prompt, allowing robots to perform different tasks or even receive updated instructions while a mission is already underway. Reinforcement learning has significantly improved the platform’s reliability: in an internal evaluation involving a 16-step mission, a supervised fine-tuned model achieved only a 38 percent end-to-end completion rate, but after reinforcement learning was applied across multiple layers of the system, completion rates increased to 90 percent.

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NVIDIA launches Halos for Robotics, industry’s first unified safety system
→ Full-stack architecture extending 18,600 engineering years of AV safety to physical AI
→ What NVIDIA IGX Thor + Holoscan Sensor Bridge + Halos OS reveal: safety built into every layer from compute to certification

Genesis AI unveils Eno, debut multi-purpose robot
→ Non-humanoid form factor with human-like hands, folding mechanism for “calm intelligence” and minimal footprint
→ Why full-stack in-house AI + optional transparency screen signal trust-first design for human-robot collaboration

Hyundai acquires SoftBank’s remaining 9.65% Boston Dynamics stake for $325M
→ Consolidates 100% ownership under Hyundai Motor Group, deepening robotics commitment
→ What put option exercise signals: transition from SoftBank’s diversified portfolio to automaker’s core automation strategy

AEON begins production tasks at BMW Leipzig plant
→ Humanoid now performing actual manufacturing work while training on future applications
→ Why shift from “can these operate in manufacturing” to “how quickly productive and scalable” marks new phase

NVIDIA announces the industry’s first safety system for Physical AI! 🛡️

NVIDIA has announced NVIDIA Halos for Robotics, the industry’s first full-stack, comprehensive safety system for robotics and physical AI that unifies AI compute and safety. The system extends NVIDIA Halos’ proven autonomous vehicle safety to robotics, giving machines that sense, decide, and act in the real world a single common safety architecture. Agility, a leading humanoid robotics company, is the first to use Halos for Robotics to build safety into its humanoids working for customers including Amazon, GXO, Schaeffler, and Toyota Motor Manufacturing Canada.

Drawing on more than 18,600 engineering years of autonomous vehicle safety development, Halos for Robotics provides developers with a common safety architecture for building, validating, and deploying physical AI systems. Safety is built into every layer: NVIDIA IGX Thor and Holoscan Sensor Bridge provide industrial-grade AI compute and sensor connectivity, the Halos OS software stack handles safety functions and applications, and the NVIDIA Halos AI Systems Inspection Lab, the world’s first ANAB-accredited program for functional and AI safety for physical AI, helps partners prepare for third-party certification.

Agility is extending its leadership in humanoid safety by integrating NVIDIA IGX Thor and Halos Core into its proprietary safe human detection system for its humanoid robot Digit. The company will also participate in the Inspection Lab to ensure Digit’s safety-related software, AI components, and cybersecurity protections meet rigorous standards such as IEC 61508, ISO 13849, and ISO/IEC TR 5469 before final third-party certification. “For humanoids to deliver value at scale, safety has to be built into the robot and validated across the entire system,” said Peggy Johnson, CEO of Agility. “This collaboration unlocks true human-robot teamwork.”

The Halos for Robotics ecosystem brings together more than 40 companies across software, embedded systems, sensors and silicon, industrial applications, and certification bodies—including Acontis, QNX, Advantech, Infineon, NXP, STMicroelectronics, Texas Instruments, FORT Robotics, and KION Group. Certification bodies including TÜV Rheinland, TÜV SÜD, UL Solutions, exida, SGS, and CertX all recognize the Inspection Lab as part of their certification process. NVIDIA Halos Core is available in early access for registered developers, and the open source Halos Outside-In Safety Blueprint is now available in early access on GitHub.

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Eno is the debut multi-purpose robot from Genesis! 👀

Genesis AI has unveiled Eno, its debut robot. Based between Paris and San Carlos and founded by robotics engineer Zhou Xian, the company has secured investment from former Google CEO Eric Schmidt, among others. While decidedly non-human in form, Eno has been explicitly designed to fulfill almost all of the mobility and agility requirements of a typical adult, eschewing the head-torso-legs form factor in favor of a blend of agility and mobility.

Eno’s most obviously analogous appendages are its arms, with disarmingly human-like gloved hands. Everything else has the soft, clinical sheen of a robotic arm or welding machine. Describing it as “radically minimalist,” the Genesis AI team started with a premise of “calm intelligence,” positing that future robots, whether at home or elsewhere, need the ability to slip silently from view when not in use. The robot has a folding mechanism with articulated panels that fold up and down to extend its height while minimizing size when not in use. The hands were developed in house and closely mimic the abilities of a human hand.

Described as a “general-purpose robot,” Eno’s full-stack AI was developed in house by Genesis AI and is said to be sophisticated enough to make it a true physical agent—one that can decipher, decode, and learn tasks just like agentic AI on a high-end smartphone. To build trust right out of the box, one option is an optional screen that shows a representation of Eno’s processes in real-time. The company stresses the importance of Eno being able to work alongside humans, rather than as a remote machine in a dark warehouse.

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Hyundai to buy SoftBank’s remaining stake in Boston Dynamics for $325 million! 💸

Hyundai Motor Group plans to buy SoftBank Group’s remaining 9.65% stake in Boston Dynamics for $325 million to make the U.S. robotics firm a wholly owned subsidiary, according to a report by South Korea’s Maeil Business Newspaper.

The report said SoftBank had told Hyundai it would like to exercise its rights to sell its remaining stake in Boston Dynamics under a put option agreed when it sold Boston Dynamics to Hyundai. Hyundai Motor Group, including Group Executive Chair Euisun Chung and affiliates Hyundai Motor, Kia, Hyundai Mobis, and Hyundai Glovis, already owns just over 90% of the robot maker.

The move would consolidate full ownership of Boston Dynamics under Hyundai, deepening the automaker’s commitment to robotics as it continues to position the company at the center of its long-term automation and physical AI strategy.

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Free Robotics Resources 👨🏼‍🏫

Learn how to use MATLAB and Simulink with platforms like VEX Robotics, LEGO and other educational robotics kits.

MathWorks experts share knowledge on elementary concepts in robotics for beginners.

Access it here for free!

AEON begins production tasks at BMW plant! 🏭

BMW Group and Hexagon Robotics have reached a new milestone in their collaboration as AEON, Hexagon Robotics’ humanoid robot, begins performing production tasks within BMW Group Plant Leipzig, while simultaneously being trained on future manufacturing applications. “Humanoid robotics is entering a new phase,” said Arnaud Robert, President of Hexagon Robotics. “The question is no longer whether these systems can operate in manufacturing environments, but how quickly they can become productive, versatile, and scalable.”

The next phase of the collaboration will focus on increasing autonomy, robustness, and operational performance. Battery assembly activities are also expected to move from the Innovation Garage into the factory environment as part of future pilot activities. Robert added that the partnership with BMW and the progressive rollout at the Leipzig plant is enabling Hexagon to demonstrate the value of humanoids in the real world.

The collaboration reflects a broader trend toward the adoption of humanoid robotics in industrial environments. According to Hexagon’s global Robot Generation study, people overwhelmingly favor the use of robots in factories, warehouses, and hazardous environments, while preferring human involvement in caregiving and education. The findings suggest strong public support for robots performing physically demanding industrial tasks when deployed with appropriate governance and safety measures.

Looking ahead, BMW Group and Hexagon Robotics are continuing to build the path from pilot systems to production-ready humanoid workforce solutions. AEON will be deployed in production by the end of 2026, marking an important step toward the next generation of manufacturing automation.

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Neura Robotics raises up to $1.4B Series C at $7B valuation
→ Tether, Qualcomm, Amazon, NVIDIA, Bosch, Schaeffler, EIB participate with conditional milestones
→ Why European robotics reaching mega-round scale signals competitive credibility alongside US/China leaders

Theker raises €80M, Europe’s largest robotics Series A
→ Barcelona-based startup builds reconfigurable robots: swappable hands, arms, form factors vs fixed humanoid design
→ Raising 2x target, growing from dozens to 120 employees, expanding showrooms across Europe, US, Asia

Robotiq ships TSF-85 tactile sensor digital twin on NVIDIA Isaac Sim
→ 97% SSIM accuracy on hyperelastic model, 90% on elastic, trained on 53,400 real-world maps
→ What 2.3M cycle durability with zero signal drift reveals: models maintain consistency through demanding factory use

Standard Bots raises $200M Series C at $1B valuation
→ AI-native robots taught by demonstration, targeting 10% US industrial deployments by next year, 100% US manufacturing by 2027
→ Why 150% job posting increase + 15% employment growth in robot-adopting plants challenges “job loss” narrative

Neura Robotics raises up to $1.4 billion in Series C! 💰

German company Neura Robotics has raised a Series C round worth up to $1.4 billion to develop its humanoid robotics technology, with U.S. tech giants Tether, Qualcomm, Amazon, and Nvidia all participating. The round also featured European industrial companies Bosch and Schaeffler, alongside the European Investment Bank. The company hit a valuation of around $7 billion, according to a source familiar with the matter.

“The future of AI will not only live on screens,” said David Reger, founder and CEO of Neura Robotics. “It will move, interact, learn and work beside us in the real world.” The full funding is contingent on Neura hitting certain milestones based on company performance, the source added.

Investors have piled into robotics startups in recent times as attention turns to deploying AI in physical systems that can interact in real-world environments. Robotics companies have raised $55.8 billion so far in 2026, according to Dealroom, a record figure nearly double the previous record raised last year. The majority has been raised by companies in the U.S. and China, but new European robotics companies are emerging too, including SoftBank-backed Agile Robots and U.K.-based Humanoid.

“Many believed globally relevant AI infrastructure companies could only emerge from Silicon Valley,” Reger said. “We believe the next generation of AI leaders can emerge anywhere in the world where there is enough vision, engineering talent and execution speed. With this financing, Neura is firmly among the global leaders in the robotics race, alongside the best in the US and China.”

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Theker just raised $85M to build the factory robots! 🦾

Theker, a Barcelona-based AI robotics startup, has raised $85 million in what it’s calling “Europe’s largest ever robotics Series A.” The company aims to go beyond robots trained for a single task. “If you always have to put the same cookie in the same box, that works perfectly, but most processes aren’t like that,” co-founder Carla Gómez Cano told TechCrunch. Theker is designed for that messier reality.

Unlike humanoid robots designed around a fixed form, think Boston Dynamics, Theker’s machines are built to be reconfigured. Their hands, arms, and overall form can be swapped out or resized depending on the task, whether that’s sorting packages, packing clothing, or handling bottles and cans in a warehouse. Inditex, Zara’s parent company, signed on as an early backer, signaling where Theker’s ambitions start—the broader goal is to move beyond retail into heavier industrial settings like manufacturing, where the complexity and scale of manual tasks is even greater.

The Series A came less than a year after a record seed round, led by American VC firm CRV and backed by a mix of traditional and strategic investors, including Samsung and Aglaé Ventures, the investment vehicle tied to LVMH chairman and CEO Bernard Arnault. Gómez Cano said Samsung is not a client yet but the two are in advanced discussions—a potential trifecta of customer, supplier, and investor that would give the startup both revenue and credibility in manufacturing at scale.

To demonstrate it can deliver, Theker has a showroom in central Barcelona and plans to open others as it expands across Europe, the U.S., and Asia. The company has already received 15,000 job applications and could grow from dozens to up to 120 people by the end of the year. Raising twice its target has reinforced the startup’s conviction in keeping its HQ in Barcelona, a growing robotics hub, and in Europe’s tech ecosystem more broadly.

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Robotiq releases tactile sensor digital twin on NVIDIA Isaac Sim! 🫆

Robotiq has released the digital twin of its TSF-85 tactile sensor in NVIDIA Isaac Sim, the first industrial-grade tactile sensor digital twin shipping on a commercial collaborative gripper. Tactile sensing promises to accelerate robotics, but its adoption has been limited by the lack of data from industry-ready hardware and accurate simulations. Model builders can now train contact-rich manipulation policies in simulation, then run them on the same physical sensor designed to operate reliably on the factory floor.

Most tactile sensors rely on a deformable contact interface, the very property that gives them sensitivity is also what makes them difficult to simulate. The TSF-85 digital twin is built around that constraint. It generates synthetic tactile maps through a custom Isaac Sim UI panel, visualizes them in real time, runs data generation at the simulation refresh rate, and exports to HDF5 for downstream training pipelines.

The simulation method is documented in two peer-reviewed publications. The first, published in Frontiers in Robotics and AI (2025), draws on a dataset of 53,400 real-world tactile maps to train, validate, and test each simulation pipeline, achieving up to 97% Structural Similarity Index Measure (SSIM) for the hyperelastic model and 90% SSIM for the elastic model on 12 unseen objects. A companion paper at ICCRT 2025 releases an open dataset of 46,200 real and synthetic tactile samples.

Simulation accuracy matters, but it only helps if the real sensor remains stable over time. The TSF-85 has been tested through 2.3 million cycles at maximum gripper force, with no significant variation in tactile signal output. This means models trained on its data can continue to rely on consistent signals for edges, shapes, textures, and geometry even after demanding real-world use.

Robotiq has been the go-to components provider for both academic research labs and industrial production for more than a decade, exactly the bridge Physical AI requires. The digital twin supports NVIDIA Isaac Sim 5.1 and is available now on GitHub.

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Free Robotics Resources 👨🏼‍🏫

This course gives a solid intro to modern robot learning, mixing classical methods with recent large-scale models.

Students begin with imitation learning, reinforcement learning, and policy optimization, then move on to topics like Vision-Language-Action (VLA) models and foundation models for robotics.

Access it here for free!

Standard Bots raises $200 million to bring jobs back to the US! 🇺🇸

Standard Bots has announced a $200 million Series C at a $1 billion valuation, with the company claiming to be the largest manufacturer of AI-native industrial robots in the United States. The pitch is counterintuitive: robots will make American manufacturers more competitive, and that will unlock more jobs. “We’ve gone from 20 million manufacturing workers in 1979 to only 13 million today,” their CEO said.

Standard Bots makes AI-native robot arms and industrial humanoid robots that can be taught by demonstrations rather than code—you show the robot the job, it watches, learns, and starts doing the job itself. The pitch is that this collapses the cost and complexity that have kept advanced automation out of reach for most American manufacturers, especially small and mid-sized shops. Customers range from Sunoco, Lockheed Martin, Amazon, NASA, and the U.S. Army to hundreds of smaller manufacturers. “AI-native robots are the essential power tool of the 21st century,” said CEO Evan Beard. “The quickest way to get to full autonomy is through deployments, collecting real-world data, and iterating as fast as possible.”

The company argues investment in robotics is key to closing the gap with China. According to the International Federation of Robotics, China installed 295,000 industrial robots in 2024, 54% of the global total, more than the rest of the world combined, and its operational robot stock now exceeds 2 million units, roughly 4.5 times that of second-place Japan. The Americas accounted for just 9% of new deployments. Standard Bots says it will deliver 10% of new U.S. industrial robot deployments by next year, designs almost all its own parts including its own actuators, and by 2027 plans to manufacture everything—from metal in to robots out—right here in America.

The $200 million Series C was led by robotics fund RoboStrategy alongside existing backer General Catalyst, a steep markup from the company’s previous $63 million raise in 2024. “Standard Bots stands out because they’ve solved one of the hardest problems in industrial automation: making robots that are not only powerful, but actually usable on the factory floor without specialized programming,” said Andrew Kang, CEO of RoboStrategy. The big question is whether adding more robots really does mean adding more jobs, Standard Bots cites a 2025 University of Minnesota study finding roughly 150% increase in job postings and 15% increase in employment in plants that adopt robots compared to non-adopters. Whether that brings the jobs back, or just the work, is the question the next few years will answer.

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OpenAI revives robotics division after 5-year pause
→ Hiring full-stack engineers targeting infrastructure robots for data centers, power grids, factories
→ Why near-term focus signals clarity: not consumer/warehouse, but skilled worker support building AI boom’s physical infrastructure

Genesis AI releases Genesis World 1.0 simulation framework
→ 89% hardware correlation, 45% smaller reality gap, 200+ hour real-world task runs in <30 minutes simulation
→ What separation of training (real) from evaluation (simulated) reveals: avoids simulator overfitting, enables systematic variable sweeps

Robotiq launches IQ for automatic robot integration
→ 5-minute Fit Check replaces weeks of manual quotes and site visits for palletizing decisions
→ Why deployment-ready workcells with validated designs in actual factory environments change SMB economics

Helsing launches Area 9, unveils RX-1 robotics platform
→ European defense tech incubator building full robotic stack in-house: hardware + software layer for autonomous research
→ What partnerships with ETH Zurich and INRIA Paris signal: developing unpredictable terrain navigation for debris-laden environments

OpenAI just made robotics its next frontier! 🤖

OpenAI is actively hiring robotics engineers as it revives its robotics ambitions, with an initial focus on robots that can help build data centers, power grids, and other critical infrastructure. On May 31, Sam Altman posted on X that OpenAI Robotics is hiring, seeking “exceptional full-stack hardware, ops, systems, and ML engineers to help us program and manufacture robots that are useful for society.” Greg Brockman, OpenAI’s co-founder and president, followed up to confirm the division is “making rapid progress towards building AI that can help people in the physical world.”

The move marks OpenAI’s strongest robotics push since shutting down its robotics team in 2021. The near-term focus is specific: robots to support skilled workers building infrastructure, not household helpers or warehouse sorters, but the workforce that will need to wire, pour, and assemble the data centers, power grids, and factories that the AI boom demands.

This is not OpenAI’s first attempt at robotics. The company ran a robotic hand project called Dactyl from 2017, most notably solving a Rubik’s Cube one-handed in 2019, before disbanding the entire robotics team in 2021. Since then, it has maintained a foothold through minority investments, the OpenAI Startup Fund led a $23.5 million round for 1X Technologies in 2023 and joined the $675 million round for Figure AI alongside Microsoft, NVIDIA, and Jeff Bezos. However, Figure subsequently ended its collaboration with OpenAI in February 2025 to build its AI models entirely in-house.

OpenAI’s robotics hardware lead Caitlin Kalinowski, who joined from Meta in November 2024, resigned on March 7, 2026, citing concerns about the company’s Pentagon deal and the lack of guardrails around domestic surveillance and lethal autonomy.

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Genesis AI releases Genesis World 1.0! 🎮

Genesis AI has released Genesis World 1.0, a comprehensive simulation framework that treats simulation not just as a data generator, but as the fundamental infrastructure layer for robotics development. The platform includes Nyx (a photorealistic rendering engine built for robotics), Genesis World (a unified physics simulator supporting rigid and deformable bodies), and Quadrants (a GPU-accelerated compiler). The key insight: simulation should accelerate the robot development cycle itself, turning model evaluation from a real-world bottleneck into a compute problem.

Genesis’s approach prioritizes evaluation first. Rather than training on simulated data, they train on real-world data but evaluate in simulation—keeping training and evaluation workflows separate. This avoids overfitting to simulator artifacts. Their simulation correlates at 89% with on-hardware rollouts, and their reality gap is 45% smaller than competing simulators. In tests, a single evaluation that would take 200+ hours of continuous robot operation in the real world runs in under 30 minutes in simulation.

The platform enables systematic evaluation across hundreds of tasks with hundreds of episodes each, revealing exactly where policies fail. They can sweep variables—object shapes, lighting angles, camera trajectories—across multiple axes simultaneously, something nearly impossible on physical hardware. This lets researchers validate design choices before committing to large-scale pretraining, saving significant compute.

Going forward, Genesis is focused on three directions: scaling post-training through millions of simulated training iterations, building hybrid simulators that merge classical physics with learned models, and enabling self-evolving physical AI where agents improve end-to-end across both simulated and real-world loops. The north star: keeping robotics development from being bottlenecked by real-world wall-clock time, moving progress at the speed of compute instead.

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Robotiq launches IQ to automate robot integration! 🔥

Robotiq has launched IQ, an AI platform that automates the entire robot integration process, attacking one of industrial robotics’ most stubborn problems. Integration has always been manual, unpredictable, and expensive. Site visits, weeks waiting for quotes, custom engineering every single time. IQ aims to change that entirely.

The platform starts with a 5-minute Fit Check, a structured assessment that tells you whether palletizing is a fit for your floor, what deployment looks like, and whether the financial return makes sense, including in 1-shift operations. No site visit required. No engineering hours upfront. No waiting weeks for a quote that may not go anywhere. Just an honest read on your opportunity before you commit to anything.

Once fit is confirmed, IQ guides the entire project, built on lessons from over 1,000 Robotiq deployments. It captures the reality of your operation, connects your team with the right people at the right step, and generates a validated workcell design simulated in your actual factory environment. Cycle time, reach, payload, all confirmed before you commit. The result: a deployment-ready palletizing workcell with predictable performance from day one.

Robotiq calls this “Automatic Integration”, the idea that deploying a robot workcell should be as predictable and repeatable as ordering one. For the thousands of small and mid-sized manufacturers who have been priced out of robotics by integration complexity and unpredictability, this changes the equation entirely. IQ was unveiled at RUC 2026 to selected partners and opens to everyone June 4th.

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Robotics maps - London 🇬🇧

The robotics startups ecosystem is growing rapidly in London. Here’s a curated map of most of London-based companies working in the physical AI space. If I missed a company you know of, let me know! :)

Helsing officially launches Area 9, pushes into robotics! 🛡️

German defense tech giant Helsing has officially unveiled Area 9, a new research division designed as a pie-in-the-sky incubator for European defense tech, think Moonshot Factory or Skunk Works, but for Europe. As part of the launch, the company also announced RX-1, a robotics research platform developed by Area 9. “Until now, robotics research in Europe meant depending on platforms built elsewhere,” said Helsing’s Chief Scientist Antoine Bordes, who is leading the project. “We built RX-1 so that doesn’t have to be true anymore. Designed here, owned here, iterated here.”

Area 9 was first announced last year, with the innovation team initially working on AI agents for the battlefield, including Centaur AI. The Centaur agent was publicly unveiled in June 2025 when Helsing and Saab announced it had flown the latter’s JAS 39 Gripen E fighter. Centaur will also serve as the AI brain for the company’s CCA analog, the CA-1 Europa, unveiled last fall. Now the company is moving into the physical realm with robotics.

RX-1, in its current form factor, looks like a robot dog. Helsing plans to build all parts of the robotic stack—the robot itself plus the software layer that researchers can develop and test different autonomy capabilities on. The platform is more research-intensive and less currently-fieldable, but Helsing says it will make RX-1 available to research institutions and robotics laboratories across Europe. The company has already teamed up with the robotics group led by Marco Hutter at ETH Zurich and INRIA Paris to further develop RX-1.

The company’s goal is to develop an autonomous robotic platform that can navigate real-world, unpredictable terrain—like debris-laden environments—built entirely in Europe. “Area 9 exists to find the problems that look impossible and break them open,” Bordes said. “That’s why we made RX-1.”

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Humanoid’s 1,000+ robot deal with Schaeffler signals 100K total shipments
→ Seven-digit actuator purchase (1M+) at 50% coverage implies 100K robots across all clients over 5 years
→ Why dual role matters: Schaeffler as both aggressive adopter (thousands of units) and preferred supplier

Boston Dynamics reveals Atlas fridge-lifting via reinforcement learning
→ Atlas trained millions of simulation hours with varied weight, friction, grip, positioning conditions
→ What proprioception demonstrates beyond cameras: sensing weight, balance, resistance for real-time unstable load adaptation

Locus Robotics acquires Nexera for AI-driven grasping
→ NeuraGrasp’s patented soft membrane + AI + vision adapts dynamically to shape, texture, material, porosity, weight variations
→ Why mobile manipulation at enterprise scale is the frontier: efficiently grasping millions of SKU types with speed/precision

Rivian spinoff Mind Robotics raises $400M, total hits $1B
→ $900M raised in 2 months at $3B+ valuation as Scaringe targets factory automation with “robotics with human-like skills”
→ Why “Project Synapse” launched: existing startups not fully equipped for industrial work

Humanoid’s 1,000+ robot deal with Schaeffler! 🏭

Two-year-old British startup Humanoid has signed a deal to integrate “a four-digit number of humanoid robots into live manufacturing operations” at German industrial giant Schaeffler’s factories starting later this year. The bigger news, however, might be that Humanoid has committed to buying a “seven-digit number of actuators” from Schaeffler over the term of the agreement, suggesting Humanoid intends to ship a staggering 100,000 humanoid robots across all of its clients over the next five years.

Four months ago, Schaeffler and Humanoid announced they’d put “several hundred” humanoid robots into factories over the next five to six years. On Wednesday, the companies upgraded the number significantly, signing an agreement to integrate at least 1,000 – and potentially many more, humanoid robots into Schaeffler’s manufacturing operations by 2032. This makes it one of the largest disclosed humanoid robot rollouts to date and arguably the most aggressive escalation between a humanoid OEM and an industrial customer.

The supply agreement indicates Schaeffler will become Humanoid’s preferred supplier, covering more than 50% of the company’s demand for joint actuators for its wheeled-based platforms through 2031. This partnership is expected to translate into the supply of a “seven-digit number of actuators” – at least one million. The wheeled HMND 01 Alpha offers close to 30 active degrees of freedom, and the “Schaeffler-relevant” actuator count per robot is probably roughly 18-22 joint actuators. At 50% coverage, one million actuators means 100,000 robots over the next five years.

Schaeffler is simultaneously one of the most aggressive industrial adopters of humanoids and a preferred component supplier for the platforms it’s buying. The company is committed to buying thousands of humanoid robots from Humanoid, at least 1,000 from Switzerland’s Hexagon Robotics, plus a bigger partnership with Germany’s Neura Robotics for 4,000-6,000 units. Schaeffler CEO Klaus Rosenfeld said the company is now engaged with roughly 45 humanoid robotics players worldwide and expects its humanoid robotics division to secure an order book in the hundreds of millions of euros by 2030.

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Boston Dynamics reveals how Atlas robot lifts a.. fridge! 🧊

Boston Dynamics has revealed how its Atlas humanoid robot learned to lift and carry heavy industrial objects using reinforcement learning and large-scale simulation training. In a newly released technical blog, the company showed Atlas rotating its torso 180 degrees, squatting to pick up a mini-fridge, and carrying it across a lab floor while adjusting to shifting weight inside the object. The behavior was developed within weeks of Atlas’ public debut earlier this year.

The latest demo marks a shift from choreographed robot movements toward adaptable industrial behaviors designed for factories, warehouses, and construction sites. Boston Dynamics said Atlas is being developed as a “general purpose tool for physical work.” Rather than relying mainly on cameras, the robot uses proprioception, internal body awareness, to sense weight, balance, grip, and resistance while moving objects, allowing Atlas to adapt in real time to unstable loads and changing conditions.

Boston Dynamics trained Atlas through reinforcement learning, where the robot repeatedly practiced the same lifting task in simulation under different conditions. The company varied factors such as object weight, floor friction, grip strength, and fridge positioning to force the robot to adapt. “Atlas practiced the moves for millions of hours in simulations in parallel on GPUs,” the company said. The process starts with a reference trajectory, then Atlas is rewarded for completing tasks correctly while external disturbances are introduced.

One major advantage of the new Atlas platform is its reduced “sim-to-real gap”—a longstanding robotics challenge where behaviors trained in simulation fail in the physical world. Boston Dynamics said Atlas’ simplified hardware architecture makes accurate simulation easier, using only two actuator types across its body with symmetrical arms and legs.

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Locus Robotics acquires Nexera Robotics! 🪬

Locus Robotics, the leader in Flexibility-First Warehouse Automation, has announced the acquisition of Nexera Robotics, a Vancouver-based robotics company specializing in advanced robotic grasping. The integration of Nexera’s proprietary NeuraGrasp end-effector technology into the Locus Robotics physical AI platform significantly expands the company’s autonomous mobile manipulation capabilities and broadens what Locus Array can handle across end-to-end fulfillment workflows.

NeuraGrasp combines AI-driven grasping intelligence, onboard sensory inputs, computer vision, and a patented soft membrane structure to adapt dynamically to the physical characteristics of each item. This enables a single gripper to conform to variations in shape, surface texture, material, porosity, and weight, creating reliable grasps across high-variability inventory found in real warehouse operations.

Locus Robotics CEO Rick Faulk stated that the frontier of warehouse robotics today is AI-driven mobile manipulation at enterprise scale, and being able to efficiently grasp millions of SKU types with both speed and precision is where the next decade of value gets created. Nexera CEO Roy Belak said they built NeuraGrasp to solve the manipulation challenges that have held robotic picking back for years, and joining Locus gives them the platform, scale, and customer base to bring this breakthrough technology into high-velocity fulfillment environments.

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Robotics maps - Europe 🇪🇺

Alpine Tech Corridor is the undisputed deeptech capital of continental Europe. The data from the European Deeptech Report 2026:

Access the report here:

Rivian spinoff Mind Robotics raises another $400M!

Rivian’s spinoff company Mind Robotics has raised another $400 million, just two months after raising $500 million, as it works to develop industrial robotics that can further automate factory operations. The funding round, first reported by the Wall Street Journal, was led by Kleiner Perkins, with participation from the venture arms of Volkswagen (partnered with Rivian on a software joint venture) and Salesforce. The new funding brings total raised to more than $1 billion and values Mind Robotics at more than $3 billion.

Rivian CEO RJ Scaringe, who is chairman of Mind Robotics, told TechCrunch in March that he created the company because he felt other startups were not fully equipped to automate industrial work. He started the project—initially known as “Project Synapse”—as an effort to build “robotics with human-like skills.” Mind Robotics had previously raised $115 million from Eclipse after it was created in 2025.

Scaringe also helped create and spin out a micromobility company called Also, which has raised more than $300 million to date. The rapid fundraising pace, $900 million in just over two months, demonstrates strong investor confidence in Mind Robotics’ vision to bring advanced industrial robotics to factory operations and manufacturing environments.

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Unitree debuts GD01 manned transformer mecha
→ 500kg bipedal-to-quadruped machine starting at $574K, world’s first mass-produced transformable mecha
→ Why 5,500+ humanoid shipments in 2025 demonstrates China’s manufacturing scale advantage: lower costs, faster scale-up

Genesis AI unveils GENE-26.5 for human-level manipulation
→ Foundation model demonstrates 20-step cooking, Rubik’s Cube solving, piano playing with proprietary hand mirroring human form
→ What 1:1:1 mapping glove with tactile e-skin reveals: 100x cheaper hardware, 5x greater data collection efficiency vs teleoperation

Apptronik stacks C-suite with Waymo, Boston Dynamics, Amazon vets
→ Chu (CPO), Garell (Services), Shah (Software), Perry (Marketing), Birtz (People Ops) join as race shifts from demos to fleet support
→ Teasing new humanoid beyond Apollo (6ft, 55 lb lift, 22hr/day, 42 DOF with DeepMind Gemini)

SoftBank creates Roze AI for automated data center construction
→ New company deploying autonomous robots to build server farms with $100B IPO target by H2 2026
→ Why automating infrastructure creation signals meta-level bet: building the physical AI boom’s foundation

China's Unitree debuts real-life transformer! ⚙️

Unitree Robotics has unveiled the GD01, a manned “mecha” capable of transitioning between bipedal walking and four-legged mode, bridging the gap between science fiction and reality. The GD01, resembling an Autobot from a Transformers movie, is a high-strength alloy machine designed for civilian transport. It weighs 500kg with a pilot on board (roughly the weight of a grand piano) and carries a starting price of 3.9 million yuan (US$573,674).

A demonstration video shows the GD01 carrying a pilot in its torso-mounted cockpit, walking like a humanoid robot and knocking over a brick wall with its hand. The machine then reconfigures its chassis, shifting into a four-legged crawl. Unitree described the robot as the world’s first mass-produced “transformable mecha.”

The GD01 expands the Hangzhou-based company’s portfolio at a time when Chinese manufacturers are significantly outstripping their US rivals, helped by lower production costs and faster manufacturing scale-up. Chinese companies accounted for nearly 90% of global humanoid sales in 2025, according to research firm Omdia. Unitree shipped more than 5,500 humanoid robots last year, demonstrating the company’s strong position in the rapidly growing robotics market.

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Genesis AI introduces a model for more dexterous robot manipulation! 🪬

Genesis AI has unveiled GENE-26.5, claiming it gives robots “human-level physical manipulation capabilities.” The San Carlos-based company also announced a system to “unlock unlimited amounts of data” and train GENE-26.5 at scale, combining two proprietary components: a new data engine and a human-scale robotic hand that enables direct skill transfer from humans to robots.

GENE-26.5 is an AI foundation model purpose-built for robotics, designed to absorb massive amounts of data and environments to enable robots to perform complex, long-horizon tasks with human-level dexterity. Demonstrations show the system performing fluid, human-like dexterity for complex tasks including cooking a 20-step meal, preparing a smoothie, conducting high-precision lab experiments, wire harnessing, solving a Rubik’s Cube with continuous in-air manipulation, single-handed multi-object grasping, and playing the piano at a human level.

The company has addressed the “embodiment gap” with proprietary hardware that mirrors the human hand in form and function, paired with a data-collection glove equipped with tactile-sensing electronic skin. When worn by a human, the glove allows for a 1:1:1 mapping between the glove, the human’s hand, and the robotic hand, allowing humans to seamlessly provide GENE-26.5 with high-quality data at scale that translates into robotic skills. The glove is 100 times cheaper than typical hardware cost and demonstrates up to five times greater data-collection efficiency than traditional teleoperation methods.

Genesis AI’s data engine taps into egocentric video data from humans wearing cameras and massive amounts of human-based internet videos. The company has also developed a simulation system with a rendering engine and hyper-realistic physics to create accurate representations of actual conditions.

Read more here!

Apptronik teases new robot, stacks C-suite with Waymo, Boston Dynamics, Amazon vets! 💼!

Apptronik, the Austin-based humanoid robot company that closed a $935 million Series A at roughly $5.3 billion valuation in February, has stacked its executive ranks with tech company veterans from Waymo, Boston Dynamics, Amazon, iRobot, and Paramount+. The new hires include Daniel Chu as Chief Product Officer (from Waymo), Kevin Garell as SVP of Services & Support (from Boston Dynamics), Chirag Shah as VP of Software (from Amazon), Dave Perry as VP of Marketing (from Paramount+), and Justin Birtz as VP of People Operations (from Cellino/iRobot).

CEO Jeff Cardenas framed the leadership refresh as representing the urgency of the moment, stating “we are at a defining moment in robotics where the technology has finally met the magnitude of the mission.” He added a significant statement: “We have the talent, the technology, and the momentum to bring AI-powered humanoid robots to the world at scale.” The race is shifting from who can demo a robot folding laundry to which companies can support fleets of humanoid robots in production environments doing real work.

Apptronik also teased a new humanoid robot, saying the company is preparing to unveil it soon. This sounds like a new robot, not just a new version of Apollo, its existing humanoid that stands roughly six feet tall, can lift up to 55 pounds, operates up to 22 hours a day, has 42 degrees of freedom, and is increasingly powered by Google DeepMind’s Gemini Robotics models. Apollo is currently deployed in commercial pilots with Mercedes-Benz, GXO Logistics, and Jabil.

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Robotics maps - Pittsburgh 👨🏼‍🏭

Pittsburgh is a good place to launch a robotics company too.

SoftBank is creating a robotics company that builds data centers! 🏗️

Japanese multinational SoftBank is reportedly creating a new company called Roze AI designed to automate data center construction in the U.S. Roze would seek to make data center construction more “efficient” by deploying autonomous robots to help build server farms, among other things. The Financial Times originally reported the news, with additional details from The Wall Street Journal.

In an interesting twist, SoftBank is already prepping Roze for an IPO, and some executives want it to happen by the second half of 2026. The desired valuation might be $100 billion, according to the Financial Times. The move comes as tech companies race to build out infrastructure that can further drive the automation boom, with SoftBank now positioning itself to automate the creation of that infrastructure itself.

Other recent ventures have also envisioned using AI and automation to make the industrial sector more efficient. For example, Amazon mogul Jeff Bezos has co-founded a startup called Project Prometheus that plans to buy firms in major industrial sectors and modernize them using AI. However, SoftBank has been known to back some dark horse startups, notably sinking hundreds of millions of dollars into Zume, an AI-driven pizza delivery startup that went belly up in 2023.

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Schaeffler expands to 1,000+ humanoid deployment by 2032
→ AEON’s successful 2025 pilot leads to multi-site rollout starting late 2026 with automated part inspection
→ Why wheel-based locomotion and unique sensor suite demonstrate high-precision manipulation in multi-machine stations

Google DeepMind launches Gemini Robotics-ER 1.6
→ Reasoning-first model achieves 93% success on instrument reading using agentic vision (visual reasoning + code execution)
→ What multi-view understanding and spatial reasoning improvements unlock: pointing, counting, success detection, gauges/sight glasses

ABB Robotics launches PoWa cobot family
→ 7-30 kg payloads, 5.8 m/s top speed bridging gap between traditional cobots and industrial robots
→ Why 20% annual cobot market growth through 2028 signals demand for speed/payload without operational rigidity

Skild acquires Fetch Robotics assets from Zebra
→ Hardware-agnostic Skild Brain integrates with warehouse platforms to transform task-specific automation into unified fulfillment ecosystem
→ What phased rollout reveals about data flywheel catch-22: semi-structured (factories/warehouses)

Schaeffler to deploy a over a thousand of humanoids! 🤯

Hexagon Robotics and Schaeffler have announced the expansion of their strategic partnership to accelerate the rollout of AEON humanoid robots across factories worldwide, following the successful completion of a joint pilot program in 2025. Schaeffler plans to deploy at least 1,000 Hexagon Robotics humanoids across its global production system by 2032. Schaeffler will also provide its high-precision actuators to AEON, further strengthening the technological alignment between the two companies.

Working with Schaeffler allows Hexagon to deliver demonstrable business value across various factory environments and scale operations. During the pilot phase, AEON used its unique sensor suite and wheel-based locomotion to demonstrate high-precision manipulation capabilities in operating a multi-machine station to load, unload, and inspect parts in real-world production environments.

Building on the successful pilot, the companies will now move to deployment across multiple sites, with additional applications such as automated part inspection targeted for rollout starting at the end of 2026. A key focus of the partnership is the generation and use of real-world production data to enable faster deployment of AEONs across new use cases and production settings.

The cooperation shows how Schaeffler combines hardware excellence with industrial application, not only developing key components but also systematically integrating robots from Hexagon into its own plants with the clear aim of boosting competitiveness through physical AI.

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Google DeepMind launches new model for physical AI! 🔍

Google DeepMind has introduced Gemini Robotics-ER 1.6, a significant upgrade to its reasoning-first model that enables robots to understand their environments with unprecedented precision. By improving spatial reasoning and multi-view understanding, the company is bringing a new level of autonomy to the next generation of physical agents. The model specializes in visual and spatial understanding, task planning, and success detection, and is now available to developers via the Gemini API and Google AI Studio.

Gemini Robotics-ER 1.6 shows significant improvement over previous versions, specifically enhancing spatial and physical reasoning capabilities such as pointing, counting, and success detection. The model unlocks a new capability: instrument reading, enabling robots to read complex gauges and sight glasses—a use case discovered through collaboration with partner Boston Dynamics. The model achieves 93% success on instrument reading tasks using agentic vision, which combines visual reasoning with code execution.

Pointing is a fundamental capability used to express spatial reasoning, relational logic, motion reasoning, and constraint compliance. The model advances multi-view reasoning, enabling better understanding of multiple camera streams and their relationships, even in dynamic or occluded environments. Success detection serves as a critical decision-making engine that allows robots to intelligently choose between retrying a failed attempt or progressing to the next stage of a plan.

Read more here!

ABB Robotics launches new PoWa cobot family! 🆕

ABB Robotics has launched its new PoWa collaborative robot family, featuring higher payloads and performance than its previous offerings. The new family addresses a long-standing gap in the market between traditional cobots, which often lack the speed and payload needed for industrial applications, and conventional industrial robots that typically work best in specialized, large-scale automation environments. ABB estimates the global cobot market will grow by 20% annually through 2028.

They said that cobots are growing significantly faster than traditional industrial robots, driven by demands from both small and midsized companies starting their automation journey as well as large enterprises. These customers are seeking higher speeds and payloads, but also greater ease of use and compact designs. Established manufacturers want to automate heavier, fast-cycle applications without the complexity and operational rigidity of traditional industrial robots.

PoWa extends ABB Robotics’ cobot offering with industrial-grade performance, including six different payload categories from 7 kg (15.4 lbs) to 30 kg (66.1 lbs), and can reach a top speed of up to 5.8 m/s (13 MPH). Purpose-built for compact environments, the line is ideally suited for applications such as high-speed machine tending, palletizing, screwdriving, and arc welding, enabling manufacturers to automate heavier and faster processes.

The new cobots are easy to use through programmable buttons on the arm-side interface and no-code programming, and are compatible with an extensive ecosystem of third-party accessories. PoWa can be unboxed and operational within an hour, enabling plug-and-play with a wide range of tools.

Read more here about it!

Robotics maps - New York City 🗽

New York City is a good place to launch a robotics company, apparently.

Skild acquires Fetch Robotics assets from Zebra! 🦓

Skild AI has acquired the robotics division of Zebra Technologies (formerly Fetch Robotics) in a move to bridge the gap between foundation AI and industrial hardware. The acquisition will help Skild deploy its “omni-bodied” intelligence layer across the global logistics sector. By integrating its hardware-agnostic AI “brain” with Zebra’s warehouse platforms, the acquisition aims to transform task-specific automation into a unified, autonomous fulfillment ecosystem. Financial details were not disclosed.

Skild CEO and co-founder Deepak Pathak told that “the Fetch Team is the main reason for the acquisition as they bring years of deployment experience.” Skild plans to support the existing Fetch/Zebra installed base, sell new Fetch robots, and integrate the Skild Brain into Fetch products to enhance the robots’ capabilities and open up new advanced manipulation solutions for the warehouse. The Zebra Symmetry platform, which coordinates and orchestrates robot work, will be deeply integrated with the Skild Brain.

The acquisition signals a pivot away from “brittle,” task-oriented robotics toward a unified intelligence layer capable of controlling diverse forms of machines. Skild is focused on building out its “data flywheel” to train AI world models. The primary differentiator is the Skild Brain, a foundation model designed to operate any machine that moves and adapt to physical changes without needing retraining from scratch.

Skild AI uses a phased rollout strategy to resolve the “catch-22” where robots need data to improve, but only capable robots can be deployed to gather data. Phase 1 focuses on semi-structured environments like factories and warehouses, Phase 2 moves into dynamic environments like hospitals and hotels, and Phase 3 aims for general-purpose robots in fully unpredictable environments like private homes. Key partners include ABB Robotics, Universal Robots, and Mobile Industrial Robots, allowing Skild to scale quickly by utilizing the hardware footprint of some of the world’s largest robotics providers.

Read more here about it!

Agibot ships 5,000 humanoids in 3 months, 10,000 total
→ Took 2 years for first 1,000 units, 1 year to reach 5,000, now 5,000 in just 3 months → Why China’s 90% global humanoid manufacturing share signals maturation: supply chain standardizing, pivot from viability to scalable value

Generalist unveils GEN-1 model: 99% success vs 64% baseline
→ Tasks completed 3x faster using only 1 hour robot data per task, trained on 500K hours real-world data
→ What pretraining on low-cost wearable human activity data reveals about avoiding expensive teleoperation datasets

Sanctuary demonstrates zero-shot in-hand manipulation
→ 21-DOF hydraulic hand reorients lettered cube 10 consecutive times without dropping, no palm support
→ Why finger abduction and proprietary hydraulic actuation matter for human-level dexterity in industrial applications

Manna raises $50M Series B, total hits $110M
→ 250K+ regulated commercial UAV flights across Ireland, Finland, Texas over 7 years with proven economics
→ What <3 minute delivery, 85% CO₂ reduction, and NPS 86 demonstrate about suburban retail unit economics

Agibot shipped 5,000 humanoids in the last 3 months! 🤯

Shanghai-based Agibot has announced it shipped 5,000 humanoid robots in the last three months alone, hitting 10,000 total robots shipped. For context, it took the company two full years to ship the first 1,000 units and another year to scale to 5,000. Now the company has surpassed that three-year total in just three months.

As the supply chain matures and manufacturing standardizes, there’s a pivot from small-scale, niche applications to robust, large-scale commercial demand. The widespread deployment of Agibot’s robots is no longer about seeking technical viability, but about delivering scalable value and driving the adoption of embodied AI.

Independent research released in January indicated that Agibot currently has the lion’s share of the global humanoid robot market. At that point, Omdia estimated the company had shipped just under 5,200 units, with second place Unitree at 4,200, and the first American humanoid robot manufacturers: Figure, Agility Robotics, and Tesla at around 150. Almost 90% of all built humanoid robots were manufactured in China, and with this new news, that percentage will only go up.

According to Agibot, these robots are not just being sold and used in China’s domestic market, with “a substantial number” active in Europe, North America, Japan, Korea, Southeast Asia, and the Middle East. The company is also seeing “a transition from initial pilot projects to repeated, large-scale rollouts.”

Read more here about it!

Generalist introduces GEN-1 model for physical AI! 🧬

Generalist AI has announced its GEN-1 general-purpose AI model for robotics, improving average success rates to 99% on tasks where previous models achieved 64%. The model also completes tasks roughly three times faster than current approaches and requires only one hour of robot data for each of these results. Founded in 2024, the San Mateo-based company is building embodied foundation models for general-purpose robots and asserts that GEN-1 “unlocks commercial viability across a broad range of applications.”

The latest release came just five months after the company released its GEN-0 model, which demonstrated that scaling laws exist in robotics. GEN-1 further scales GEN-0’s foundation and uses algorithmic advances to start mastering simple tasks. The company trained the model from scratch on its dataset of half a million hours of real-world data. While Generalist was optimistic about the AI model’s progress, it noted that GEN-1 can’t solve all tasks and some would require higher than 99% success rates to be useful in real settings.

Previous general models in robotics that surpass 90% success have depended on enormous teleoperation datasets that are expensive and difficult to scale. Instead, for GEN-0 and GEN-1, the base foundation model is trained without any robot data. The model uses data from low-cost wearable devices on humans doing millions of activities, and Generalist says this pretraining can lead to high levels of mastery without requiring large teleoperation or simulation datasets.

GEN-1 includes pre-training innovations that improved compute efficiency, advances in post-training techniques, learning from experience, multimodal human guidance, and new inference-time techniques.

Read more here!

Sanctuary’s hand demonstrates zero-shot manipulation! 🪬

Sanctuary AI has demonstrated its approach to training dexterous manipulation policies for its robotic hands. In its latest video, the company’s hydraulic hand autonomously manipulated a lettered cube, continuously reorienting it to match a specified goal. The system successfully achieved the target orientation 10 consecutive times without dropping the cube, highlighting robust in-hand manipulation capabilities. The manipulation takes place entirely at the fingertips without palm support, requiring the hand to simultaneously and stably grasp the object while making progress toward the orientation goal.

Sanctuary AI claims the high number of degrees of freedom in its proprietary robotic hands (21-degrees-of-freedom) enable finger abduction and sophisticated in-hand manipulation that most current robotic hands cannot do. The Vancouver-based company also uses hydraulic actuation, which provides increased strength, speed, and control. Combined with compact hydraulic valves, this advancement offers a promising path toward achieving human-level dexterity for industrial applications and forms the foundation for precise insertion, tool use, and other real-world dexterous tasks.

Simulating anthropomorphic hands remains a significant challenge due to their complexity, contact dynamics, and high degrees of freedom. The success of this transfer highlights Sanctuary AI’s ability to build highly realistic training environments.

Read more here about it!

Universities by number of alumni-founded startups! 👨🏼‍🎓

There’s another reason to study the ETH and EPFL models closely as both universities are also European leaders in spawning startups.

Source of this stat (Dealroom)

Manna raises $50M Series B to expand in the US! 🚁

Manna Air Delivery, a global leader in consumer drone delivery, has raised $50 million in Series B funding to scale its proven operations in the United States and Europe, bringing total funding to $110 million. Manna now operates one of the most active consumer drone delivery networks in the world, with more than 250,000 regulated commercial UAV flights completed.

As a UAV delivery pioneer, Manna has operated in six locations across Ireland, as well as in Finland and Texas over the past seven years, delivering items including books, medical supplies, food, and clothing. The company’s UAV operations are designed to make communities more accessible while making last-mile delivery greener, safer, and more efficient. Recently, Manna announced a partnership with Uber, adding to existing partnerships with Deliveroo, Just Eat, and DoorDash.

CEO and Founder Bobby Healy said the company has proven the technology and economics of autonomous delivery and is now focused on scale. Manna plans to launch up to 40 new bases in the United States as it builds the infrastructure for the next generation of logistics. Orhan Gazelle, Managing Director at Schooner Capital, said Manna is building one of the most compelling real-world applications of autonomous technology, demonstrating that drone delivery can operate safely, efficiently, and at scale with the potential to fundamentally reshape last-mile logistics.

For customers, Manna’s UAV technology delivers orders in under three minutes while reducing road congestion and cutting CO₂ emissions by up to 85% compared with road-based delivery. Deliveries are lowered safely to customers’ gardens or driveways using recyclable packaging and biodegradable tethers. Popular with families, people with mobility issues, and those working from home, Manna has achieved a Net Promoter Score of 86, placing it among the highest-rated services in consumer technology. Manna was one of the first drone delivery companies globally to demonstrate positive unit economics for suburban retail delivery.

Read more here about it!

Amazon acquires Fauna Robotics for consumer humanoids
→ Former Meta/Google engineers’ Sprout ($50K, 3.5 ft tall) joins Amazon after Disney and Boston Dynamics signed as early customers
→ Why M&A strategy signals shift from warehouse-only focus: Rivr for doorstep delivery, Fauna for home/business

Niryo unveils Nate modular robotics platform
→ Open interfaces for AI integration with 3-month iteration cycles vs rigid traditional automation
→ What €40K starting price vs €70K comparable systems reveals about cost and integration time as real blockers

Agile Robots partners with Google DeepMind
→ Gemini Robotics foundation models integrating with 20,000+ installed robotics solutions worldwide
→ Why iterative deployment cycle creates AI flywheel: real-world data improves models, models expand capabilities, unlocking broader deployment

Lucid Bots raises $20M Series B, operator network hits $75M revenue
→ Deployments surge from 100 to 700 units in 5 months, Sherpa drone with power tether pre-orders hit $1M
→ What 2-5x job completion speedup and <2 month payback periods demonstrate about operator economics

Amazon acquires humanoid maker Fauna Robotics! 🤖

Amazon has acquired Fauna Robotics, a startup that builds “approachable” humanoid robots for consumers and businesses, the company confirmed Tuesday. Terms of the deal weren’t disclosed. An Amazon spokesperson said the company is excited about Fauna’s vision to build capable, safe, and fun robots for everyone, and together with Amazon’s robotics expertise and decades of experience earning customer trust in the home, they’re looking forward to inventing new ways to make customers’ lives better and easier.

Fauna Robotics was founded in 2024 by former Meta and Google engineers. Earlier this year, the New York-based company launched Sprout, a $50,000 bipedal robot that’s 3 feet, 6 inches tall, weighs 50 pounds, and is designed to be “approachable and human-friendly” as well as “genuinely accessible” to software developers. The company signed up Disney and Hyundai’s Boston Dynamics as early customers. Fauna’s roughly 50 employees will join Amazon in New York, and the company will operate as Fauna Robotics, an Amazon company.

Amazon has spent more than a decade investing in robotics, primarily for warehouse operations. It acquired Kiva Systems for $775 million in 2012, which became the foundation for Amazon Robotics. The company has turned to M&A again to beef up its robotics expertise, last week, Amazon acquired Rivr, a Swiss robotics company developing machines for “doorstep delivery.”

The company has experimented with home robots before, launching a squat, roving personal robot called Astro in 2021, priced at $1,600 and available only by invitation. By acquiring Fauna, Amazon is entering the increasingly crowded humanoid robot market.

Read more here!

Founder of Fauna Robotics with its humanoid bot called Sprout

Niryo introduces Nate, a modular robotics platform for industrial Physical AI 🔧

At Global Industrie 2026, Niryo presented Nate (Niryo to Automate), a new modular robotics platform designed to make AI-driven automation easier to deploy in real factories. Instead of fixed, hard-to-modify systems, Nate is built as a set of interchangeable building blocks: robotic arms, actuators, grippers, and software modules that can be combined depending on the application. The key idea is not a single robot, but an infrastructure layer for industrial robotics.

The platform offers modular hardware for faster configuration and reconfiguration, open interfaces for easier integration of AI models (vision, control, optimization), and short iteration cycles enabling new robot designs in roughly three months. This directly targets a real issue in manufacturing: traditional automation is too rigid for high-mix, fast-changing production.

Nate is already being used to build pre-configured robotic cells called “Smart Machines” for common factory tasks like machine tending, end-of-line packaging, and sorting and quality control. Each system combines a 6-axis robot arm, gripper and workstation, application software, and optional AI modules. The goal is simple: reduce integration time and engineering effort while still allowing flexibility.

Nate is positioned as a bridge between robotics and AI systems. AI models for vision and control can run directly on the platform, systems can adapt to part variability (not just repeat fixed motions), and make real-time adjustments based on production conditions. On the hardware side, Niryo introduced new actuators and a 6-axis NR Series robot with target payloads up to 20 kg, enabling palletizing and heavier tasks.

Niryo claims systems start around €40,000, compared to roughly €70,000 for similar setups. This is important because cost and integration time are still the main blockers for automation, not capability alone. Nate fits into a broader shift from closed industrial robots to open, modular platforms, from fixed automation to adaptable, AI-driven systems, and from long integration cycles to faster deployment and iteration.

Read more here about it!

Agile Robots to deploy Google DeepMind foundation models on its humanoid! 🤝

Agile Robots has announced a partnership with Google DeepMind, Alphabet’s AI research lab, to combine DeepMind’s Gemini Robotics foundation models with Agile Robots’ scalable industrial robotics platform. Founder and CEO Zhaopeng Chen stated that Agile Robots has already installed over 20,000 robotics solutions worldwide, proving intelligent automation at scale. The huge opportunity lies in autonomous, intelligent production systems that can transform entire industries, and integrating Gemini Robotics models positions the company at the cutting edge of this rapidly growing market.

Agile ONE is designed to work safely and efficiently alongside people and other systems in industrial settings. By bringing together Agile Robots’ hardware and AI systems with DeepMind’s Gemini Robotics foundation models, the teams hope to improve performance through robot deployment, data collection, model training, and iteration.

The collaboration is structured as a series of development and deployment phases focused on improving robotic capabilities in real-world environments. The teams will work in an iterative cycle, continuously testing, learning, and improving system performance. The partners will first focus on high-value industrial use cases in sectors with acute and growing demand for adaptable, reliable automation, including industrial and manufacturing tasks where reliability and scale are critical.

Jointly training, deploying, and testing robots will create a scalable “AI flywheel,” where data from real-world operations will improve the models, and the models will then expand robotic capabilities, unlocking broader deployment.

Read more here about it!

Robotics maps #8 (Shanghai, China 🇨🇳)

Fantastic graphic about humanoid ecosystem in the Shanghai area prepared by RoboTuo.

Lucid Bots raises Series B funding as operator network hits $75M in revenue! 🚁

Lucid Bots, an autonomous exterior cleaning company, has raised an oversubscribed $20 million Series B round co-led by Cubit Capital and Idea Fund Partners, bringing total funding to $34 million. The Charlotte-based company will use the funding for commercial operations, expanding domestic manufacturing capacity, and accelerating the rollout of Lucid Refresh, its full-service subscription platform for exterior cleaning operators. The company has experienced rapid growth, increasing deployments from 100 to nearly 700 units in less than five months.

Lucid Bots has launched several new features for its Sherpa drone, which now supports spraying of sealing/coating additives and paint. The system can be equipped with a power tether for unlimited flight time, eliminating the battery to make drones more efficient in the air and able to lift more payload. The power tether option has garnered nearly $1 million in pre-orders. The company also introduced Lavo AI, an autonomous ground-based power-washing mobile robot designed for sidewalks, driveways, and parking lots.

Lucid Bots’ systems and operator network have generated more than $75 million in exterior cleaning revenue. The Sherpa platform can reduce job completion time by two to five times, with operators reporting payback periods of less than two months. With nearly 1,000 robots in active operator hands and 93% of new business from inbound interest, the company generated as much revenue in 2025 as in its first seven years combined. The customer base spans independent operators to enterprise accounts including Disney, Sunbelt Rentals, and commercial facilities managers across the U.S.

Read more here about it!

Lucid Bots drones for exterior cleaning tasks

Amazon acquires RIVR for stair-climbing delivery robots
→ Zurich-based startup’s doorstep delivery tech gets Amazon’s infrastructure to scale faster
→ Why Amazon Industrial Innovation Fund + Bezos Expeditions seed investment signaled strategic interest early

RoboForce raises $52M, total hits $67M
→ Robo-Labor platform targeting dirty, dangerous jobs across solar, data centers, mining, logistics, manufacturing, shipping
→ What foundation model + physical AI agents demonstrate about coordinating complex tasks reliably

Unitree files for $610M Shanghai IPO
→ China’s leading robot maker targeting STAR board listing to fund AI model research and manufacturing expansion
→ Why domestic IPO signals growing market confidence in robotics sector beyond VC rounds

Gecko Robotics scores $71M Navy deal for ship repair
→ Why software-only approach fails: AI decisions only as good as infrastructure data inputs
→ Supporting 80% fleet readiness by 2027 as U.S. races to modernize amid China shipbuilding gap

Amazon acquires RIVR, maker of delivery robots! 📦

Amazon has acquired Rivr, a Zurich-based autonomous robotics startup known for its stair-climbing delivery robot, in a deal that signals the e-commerce giant’s interest in doorstep delivery. Terms of the deal weren’t disclosed.

CEO and co-founder Marko Bjelonic said the acquisition will “accelerate our vision of building General Physical AI through doorstep delivery, bringing robotics and AI closer to real-world deployment at scale.” In plain terms, Amazon’s resources should help Rivr get its robots onto more doorsteps, faster. Last year, Rivr launched a pilot program in Austin with Veho, a package delivery company.

Rivr got Amazon’s attention long before its pilot program. The Amazon Industrial Innovation Fund and Bezos Expeditions invested in Rivr as part of a $22.2 million seed round that closed in 2024. The startup, which had raised a total of $25 million, was last valued at $100 million. The acquisition brings together Amazon’s massive delivery infrastructure with Rivr’s specialized robotics technology for navigating stairs and complex terrain.

Read more here!

The delivery bot from RIVR

RoboForce grabs $52M to unleash AI robots on dirty, and dangerous jobs! 🏗️

Industrial robotics startup RoboForce has secured $52 million in an oversubscribed funding round, bringing its total capital raised to $67 million. The round was led by YZi Labs, a $10 billion fund, with participation from Jerry Yang alongside existing investors including Myron Scholes, Gary Rieschel, and Carnegie Mellon University.

Founded in 2023 , the company is positioning its technology to address the growing gap in industrial labor, where physically demanding work is becoming harder to staff. Its platform focuses on Robo-Labor, autonomous machines designed to handle high-risk and physically intensive work across utility-scale solar installations, data centers, mining, logistics hubs, manufacturing sites, and global shipping operations.

At the core of RoboForce’s technology is a robotics foundation model combined with a stack of physical AI agents that control robot behavior. These agents interpret real-world data and coordinate actions across the robot’s systems, enabling machines to carry out complex physical tasks reliably. The platform continuously improves through a data-driven learning loop, where information from deployed robots feeds back into the system and combines with high-fidelity simulation to refine policies and behaviors. RoboForce is working with NVIDIA to power its computing and simulation infrastructure.

Read more here about it!

Chinese robot maker seeks $610M in Shanghai IPO! 📈

China’s leading robot company, Unitree Robotics, has filed for an initial public offering in Shanghai, moving closer to a domestic listing as investor interest in the robotics sector builds. Unitree plans to raise about 4.2 billion yuan ($610 million) on the STAR board, according to a prospectus released on Friday.

The proceeds will be used to fund research into AI models for robotics, develop new products, and expand manufacturing facilities. Unitree is known for its high-performance quadruped robots and humanoid systems, having recently unveiled models like the As2 quadruped that can carry up to 143 pounds and run at speeds approaching 11 mph.

The IPO filing signals growing market confidence in the robotics sector and represents a major milestone for Unitree as it scales production and development. The company has positioned itself as a leader in affordable, high-performance robotics for both research and commercial applications.

Read more here about it!

Unitree Robotics CEO and founder with a H1 humanoid robot

Robotics maps #7 (Hangzhou, China 🇨🇳)

Fantastic graphic about humanoid ecosystem in the Hangzhou area prepared by RoboTuo.

Gecko Robotics brings its robots to U.S. Navy ship repair in latest deal! 🚢

Gecko Robotics, a Pittsburgh-based robotics startup, has announced a $71 million deal with the U.S. Navy to cut down ship repair time as the government races to reindustrialize America’s aging defense systems. The company says its robots, capable of flying, swimming, and climbing critical infrastructure, use cameras and sensors to condense a three-month process down to as little as two days. Gecko also claims the robots can assess necessary maintenance 50 times faster than other manual techniques.

Co-founder and CEO Jake Loosararian told CNBC that this technology was never possible before, and it’s the reason why it’s taken 18 months to get a destroyer out of dry dock. He emphasized this is no longer acceptable and that Gecko will support the Navy’s goal of 80% fleet readiness by 2027, streamlining ship production so soldiers can focus on fighting and other threats. Loosararian stressed that software alone is not enough, the ability to use AI to predict and make decisions is only as good as the data inputs, representing a fundamental shift never before achieved by any robotics company in the military.

The U.S. is increasing its reliance on defense technology startups like Gecko as it seeks to modernize dated military systems amid rising geopolitical tensions. These companies are disrupting traditional defense contractors with innovative AI and autonomous tech solutions. Since taking office, President Trump has prioritized scaling and restoring U.S. shipbuilding capabilities, which have long lagged behind China, with the administration releasing a multipage plan last month to resurrect the struggling sector.

Over the years, Gecko has teamed up with mining, manufacturing, energy, and defense businesses to improve aging equipment and slash repair times, including partnerships with L3Harris Technologies, independent power operator NAES, and mining giant Freeport-McMoRan. Gecko was last valued at $1.25 billion in a $125 million funding round in June and ranked No. 30 on last year’s CNBC Disruptor 50 list.

Read more here about it!

Gecko Robotics wall-climbing robots are helping to maintain ships

Travis Kalanick launches Atoms for specialized robotics
→ Uber founder rolling CloudKitchens into new company targeting food, mining, and transportation with wheelbases vs humanoids
→ Why Pronto acquisition signals industrial autonomy as “main jam” over consumer robotaxis

Hexagon study reveals global robot anxiety divide
→ 18,000+ surveyed across 9 countries: UK adults most worried (52%), South Korea least (29%)
→ What robot density correlation demonstrates: China and South Korea’s high exposure drives lower worry, greater excitement

BMW’s 20-year automation journey reaches Physical AI era
→ 800+ Smart Transport Robots, 10-month Figure pilot producing 30K X3s, new Munich Competence Center for global rollout
→ Why imitation learning from human demos (including failures) matters more than hard-scripted programming

RealSense demos autonomous humanoid navigation
→ Dense 3D depth perception enabling localization, mapping, collision avoidance for legged robots in unstructured environments
→ What Intel spinout’s vSLAM + NVIDIA CuVSLAM reveals about shifting contact points and non-linear motion complexity

Travis Kalanick launches new company called Atoms focused on robotics! 🤖

Uber founder Travis Kalanick has launched a new company called Atoms focused on robotics that will operate in the food, mining, and transportation industries. Kalanick is rolling his existing ghost kitchen company, CloudKitchens, into Atoms. The company’s website says it will build a “wheelbase for robots,” and Kalanick said in a live interview with TBPN that his company will apply this wheelbase to “specialized robots”, not humanoids.

Kalanick emphasized that humanoids have their place, but there’s a lot of room for specialized robots that do things in an efficient, industrial-scale kind of way. To support the mining business, Kalanick revealed he’s on the precipice of acquiring Pronto, the autonomous vehicle startup focused on industrial and mining sites created by his former Uber colleague Anthony Levandowski. Kalanick is already the “largest investor” in Pronto and told TBPN that “the industrial thing is sort of like, probably, our main jam.”

Earlier Friday, The Information reported Kalanick was getting back into self-driving vehicles with “major backing” from Uber, and that he has reportedly told people he “wants to be more aggressive in rolling out self-driving technology than Waymo.” Atoms’ website makes no mention of Uber. Kalanick demurred on the idea of using Atoms robots to move people, at least in the near-term, noting that “once you crack movement in the physical world, there’s lots of people who want access to that.”

Kalanick resigned from Uber in 2017 after a confluence of crises including complaints of sexual harassment and discrimination. Before that, Kalanick had created a self-driving division at Uber in 2015, with Levandowski playing a big role after being lured from Google. Uber was sued by Google for stealing secrets related to its self-driving car project, and Levandowski was criminally charged and sentenced to 18 months in prison (later pardoned by President Trump). In a rare interview in March 2025, Kalanick expressed regret that Uber had abandoned developing its own self-driving cars.

Read more here!

ex-Uber CEO is founding a physical AI company - Atoms

Hexagon’s Robot Generation study reveals global divide in robot anxiety! 🌐

Hexagon has released a global study examining how attitudes toward robots vary sharply across generations and cultures, surveying over 18,000 adults and children across nine countries.

The research uncovers a striking truth: more than cost or technology, it’s people who ultimately determine the pace of robotic adoption, and these societal differences will set the industrial automation trajectory.

Cultural exposure and public attitudes dictate whether countries lead or lag in adoption. Hexagon CTO Burkhard Boeckem notes that trust is built through experience and clear boundaries, when people understand what robots are for and what they are not, confidence follows.

The study reveals that worry about robots is highest among adults in the UK (52%) and lowest in South Korea (29%). Children are 10% less worried about robots than adults, and comfort is highest when people imagine robots in factories or warehouses (63%). Hacking is the top concern for adults (51%), ahead of replacing humans at work (41%) and trust (26%). China is the most emotionally positive nation regarding robots, while Switzerland is the least emotionally positive.

China and South Korea have some of the highest robot densities in the world, and in these markets, greater exposure translates into noticeably lower levels of worry and much greater excitement. This points to a clear relationship, as robot density rises, so does familiarity, and with it, more positive sentiment. Exposure and attitude reinforce and accelerate each other.

The study shows robot anxiety is context dependent, highest where robots are least visible, and falls when people can see robots working safely alongside humans.

Read more here about it!

Top 10 Lessons from BMW Group Event! 🔥

1. BMW has pursued a 20+ year automation journey, evolving from 1970s industrial robot arms to sophisticated physical AI systems capable of autonomous decision-making in complex manufacturing environments.

2. Rather than manufacturing robots themselves, BMW focuses on developing the intelligence layer — creating manufacturing-specific AI knowledge that enables robots to understand and navigate BMW’s unique production requirements.

3. BMW has deployed over 800 Smart Transport Robots (STRs) across its global plants, capable of autonomous navigation without fixed routes and delivering parts just-in-sequence throughout the production network.

4. Through a partnership with Boston Dynamics, BMW’s Spot maintenance robot uses visual, thermal, and acoustic sensors for predictive maintenance, feeding real-time data directly into digital twin creation.

5. BMW’s 10-month humanoid robot pilot at its Spartanburg plant, conducted in partnership with Figure AI, supported the production of over 30,000 BMW X3 vehicles using a two-robot tandem system capable of full 10-hour shifts.

6. BMW has launched a new Physical AI Competence Center in Munich built around three pillars — industrialization, research and technology, and network partnerships — designed to replace isolated pilot projects with a structured global rollout strategy.

7. A new European partnership with Hexagon Robotics introduces the Aeon humanoid robot to BMW’s Leipzig plant, with permanent pilot operations targeting battery assembly beginning in summer 2026.

8. BMW trains its robots using imitation learning, where humans demonstrate tasks through teleoperation across multiple repetitions — including failure scenarios — shifting away from traditional hard-scripted programming toward adaptive, learning-based behavior.

9. Foundation models for BMW’s manufacturing robots are trained on three input streams — text/speech instructions, visual camera data, and robot sensor feedback — generating sequential task actions in a manner analogous to how language models produce text.

10. Leveraging a partnership with NVIDIA and millimeter-accurate digital twins of all its facilities worldwide, BMW embeds AI agents into virtual planning environments to support real-time collaborative decision-making across robotics, logistics, and ergonomics on a global scale.

Me and the Hexagon Robotics bots at the BMW Group event

Robotics maps #7 (USA 🇺🇸)

Fantastic graphic about humanoid ecosystem in the US.

RealSense unveils autonomous humanoid navigation! 👁️

RealSense demonstrated autonomous humanoid navigation with LimX Dynamics at NVIDIA GTC this week, claiming it as the first demonstration of its kind. The company emphasized that as robots including humanoids move from novelty to everyday reality, perception is no longer just about performance—it’s about safety. CEO Nadav Orbach stated that if robots are going to work safely beside humans, perception must function as the robot’s visual cortex, enabling accurate localization, collision avoidance, terrain understanding, and stable motion in unstructured environments.

Spun out from Intel last year, the Cupertino-based company delivers depth cameras and vision systems for autonomous mobile robots, humanoids, industrial automation, healthcare, and access control. The demonstration shows how dense 3D depth perception enables legged robots to localize, map, and navigate autonomously, safely, and predictably. The system uses RealSense depth cameras and vSLAM (visual simultaneous localization and mapping) integrated with odometry from NVIDIA CuVSLAM, with NVIDIA Isaac Lab accelerating development and serving as a high-fidelity digital proving ground for reinforcement learning.

RealSense explained that wheeled robots move on a relatively predictable plane, but humanoids and quadrupeds are far more complex, moving through full 3D space with shifting contact points and non-linear motion requiring stable foot placement and environmental awareness. Encoder-only odometry and 2D lidar used for wheeled robots lack the full 3D awareness needed for stable, safe motion—a gap that has constrained deployment and forced many legged robots to rely on teleoperation, supervision, or tightly controlled environments.

The partners demonstrated how RealSense’s dense depth perception fused with NVIDIA visual odometry and cuVSLAM provides scene understanding, localization, mapping, and navigation. This enables safer robotics through accurate localization and mapping, collision avoidance with people and moving objects, fall prevention through 3D terrain awareness, and predictable, human-readable motion. Dense 3D perception also unlocks behaviors including stair navigation, curb and elevation detection, uneven terrain traversal, and dynamic obstacle avoidance in shared spaces.

Read more here about it!

LimX Dynamics robot enabled with RealSense technology

Dexterity’s Foresight world model tackles truck loading
→ 4D box packing agent reasons across 3 spatial dimensions + time, making placement decisions in <400ms
→ What 100M+ autonomous production actions reveal about real-time physical manipulation vs observational models

ABB Robotics partners with NVIDIA for industrial Physical AI
→ RobotStudio HyperReality delivers 99% simulation-to-reality correlation, cuts deployment costs 40%, accelerates time-to-market 50%
→ Why exporting fully parameterized stations as USD files into Omniverse matters for synthetic data training

Yann LeCun’s AMI Labs raises $1.03B at $3.5B valuation
→ World models learning from reality vs language, starting with fundamental research not quick product releases
→ Why Nabla partnership signals focus on real-world applications where LLM hallucinations have life-threatening repercussions

Rhoda AI raises $450M Series A at $1.7B valuation
→ FutureVision platform learns from hundreds of millions of internet videos to predict physical world behavior
→ What anticipating actions dozens of times per second demonstrates about handling industrial unpredictability

Dexterity delivers a big leap for truck loading! 🚛

Dexterity has announced a major leap forward in its Physical AI stack, anchored by Foresight, a state-of-the-art world model and a 4D box packing agent. These advancements help solve some of the most physically demanding and hardest-to-staff tasks, such as truck loading.

Foresight is Dexterity’s physics-consistent world model, a real-time, transactable representation of the physical environment that enables robots to perceive, reason, and act. It represents a new class of world model, built not for observation, but for physical manipulation at production scale. In autonomous truck loading, Foresight powers Dexterity’s dual-armed superhumanoid robot, Mech, with a 4D box packing agent that reasons across three spatial dimensions plus time, determining where to place each package onto an evolving wall of freight.

This is a combinatorial problem far more complex than the game of Go, with near-infinite input variation, up to 400 potential placements per box, and multiple walls packed simultaneously. Foresight makes each placement decision in under 400 milliseconds, jointly optimizing density, stability, reachability, and dual-arm parallelism, while predicting how each placement affects the integrity of the entire truck. To date, Foresight has been trained with experience from over 100 million autonomous actions in production.

Built on Foresight, Dexterity’s agentic framework coordinates perception, decision, and motion agents that operate asynchronously to automate truck loading, package sortation, and other applications. The architecture is interpretable and safety-first, giving operators visibility into why the system makes each decision

Read more here!

Foresight, a world model developed by Dexterity

ABB Robotics partners with NVIDIA to deliver an industrial-grade Physical AI at scale! 👾

ABB Robotics and NVIDIA have announced a breakthrough partnership that brings industrial-grade physical AI to the factory floor. By integrating NVIDIA Omniverse libraries directly into its RobotStudio programming and simulation suite, ABB Robotics will deliver physically accurate simulation capabilities, dramatically cutting engineering time, reducing deployment costs by up to 40%, and accelerating time to market by as much as 50%.

The new product, RobotStudio HyperReality, will be available in the second half of 2026 and is already drawing strong interest from ABB’s global customer base. Early pilots include Foxconn, the world’s largest electronics manufacturer, and Workr, a U.S.-based robotic workforce company bringing advanced automation to small and medium-size manufacturers. RobotStudio HyperReality exports a fully parameterized robot station: robots, sensors, lighting, kinematics, and parts, as a USD file into NVIDIA Omniverse. There, ABB Robotics’ virtual controller runs the same firmware as the physical robot, enabling 99% correlation between simulation and real-world behavior.

Foxconn is piloting the technology in consumer electronics assembly, where delicate metal components and frequent product variations make automation challenging. Using HyperReality, Foxconn trains robots virtually with synthetic data, achieving high accuracy when deployed on the production line while reducing setup time and eliminating costly physical testing. Workr is integrating its WorkrCore physical AI platform with ABB industrial robots trained with synthetic data to deploy advanced automation to SMBs, planning to demonstrate AI-powered robotic systems at NVIDIA GTC 2026 that can onboard new parts in minutes without programming expertise.

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Yann LeCun's AMI Labs raises $1.03 billion to build world models! 🌍

AMI Labs, the new venture cofounded by Turing Prize winner Yann LeCun after he left Meta, has raised $1.03 billion at a $3.5 billion pre-money valuation. AMI is working on world models, or AI that learns from reality, not just from language. The round was co-led by Cathay Innovation, Greycroft, Hiro Capital, HV Capital, and Bezos Expeditions, with participation from NVIDIA, Samsung, Sea, Temasek, Toyota Ventures, and angels including Tim and Rosemary Berners-Lee, Jim Breyer, Mark Cuban, Xavier Niel, and Eric Schmidt.

CEO Alexandre LeBrun predicts that “world models” will be the next buzzword, with every company calling itself a world model to raise funding within six months. However, he believes AMI Labs is fundamentally different, with a goal to understand the real world. The startup’s first partner will be Nabla, the digital health startup where LeBrun serves as chairman. As Nabla’s CEO, LeBrun reached the same conclusion as LeCun on the limitations of large language models where hallucinations could have life-threatening repercussions.

AMI Labs is a very ambitious project that starts with fundamental research, not a typical applied AI startup that can release a product in three months. It could take years for world models to go from theory to commercial applications. The French AI lab was reportedly seeking just €500 million last December but ended up raising some €890 million, likely thanks to its team. In addition to LeCun as chairman and LeBrun’s entrepreneurial track record, the team includes Meta’s VP for Europe Laurent Solly as COO, and researchers Saining Xie as chief science officer, Pascale Fung as chief research & innovation officer, and Michael Rabbat as VP of world models.

The funding will give AMI Labs runway to bankroll its two main cost centers: compute and talent. LeBrun will prioritize quality over quantity to build the team in four key locations: Paris (headquarters), New York (where LeCun teaches at NYU), Montreal (where Rabbat is based), and Singapore (to recruit AI talent and be close to future clients in Asia). Although AMI Labs has no plans to generate revenue for now, it plans to engage with prospective customers early on, putting models in real-world situations with real data and real evaluations.

Read more here about it!

Robotics maps #6 (Shenzhen 🇨🇳)

Fantastic graphic about humanoid ecosystem in Shenzhen.

Rhoda AI raises $450M at $1.7B valuation, unveils robot intelligence platform! 💾

Rhoda AI has raised $450 million in a Series A funding round that values the company at $1.7 billion and unveiled FutureVision, a robot intelligence system it says can handle the unpredictability of industrial environments. The round drew backing from Khosla Ventures, Temasek, Mayfield, Premji Invest, and Capricorn Investment Group, among others.

Rhoda’s FutureVision platform works by first studying hundreds of millions of internet videos to learn how objects move and how the physical world behaves. It then uses that knowledge to constantly anticipate what is about to happen around it and translate those predictions into physical movements, a cycle it repeats dozens of times per second. The approach targets a longstanding problem in robotics: most machines perform well in controlled, predictable environments but struggle when something unexpected happens.

The company, which emerged from stealth on Tuesday, says its platform is designed to integrate with a wide range of robotic hardware, allowing manufacturers and logistics operators to deploy intelligent robots without rebuilding existing systems. Rhoda expects to eventually license FutureVision to companies running robotic hardware and software platforms.

Breakthroughs in AI models that help robots understand language, interpret visual information, and predict how the physical world behaves, combined with growing investment from major tech and robotics companies, are driving robotics adoption.

Read more here about it!

Rhoda AI team with their robots

BMW deploys humanoid robots in Europe for first time
→ Hexagon’s AEON tested at Leipzig plant for high-voltage battery assembly and EV production
→ Why April 2026 broader test and summer pilot signal shift from lab to stable shift operation

RLWRLD raises $26M, total seed hits $42M
→ Physical AI foundation models trained on real factory/warehouse data vs clean lab settings
→ What multimodal data from unpredictable live operations reveals about simulation limitations

Wayve closes $1.2B Series D at $8.6B valuation
→ End-to-end AI Driver runs entirely onboard, zero-shot deployment in 500+ cities across 70 countries
→ Why licensing to automakers vs vertical integration matters for capital-light scaling

Unitree unveils As2 quadruped robot
→ 5 m/s top speed, 4+ hour runtime unloaded, 20 km range with 648Wh battery
→ What 90 N·m joint torque and 5 N·m/kg torque-to-weight ratio demonstrate for payload strength

BMW deploys humanoid robots in Europe for the first time! 🇪🇺

BMW is placing humanoid robots on the factory line at its Leipzig plant, marking the first time the company has deployed them at a European facility. A small fleet of Hexagon Robotics humanoids is being tested on assembly lines and in high-voltage battery production for electric vehicles, an area where employees currently wear cumbersome protective gear. A broader test deployment is planned for April 2026, with a full-scale pilot launching in summer 2026.

Hexagon’s humanoid, AEON (unveiled in June 2025), features a human-like body that can accommodate various hand, gripper, and scanning attachments, and moves dynamically on wheels. The Leipzig pilot began in December 2025, focusing on high-voltage battery assembly and component manufacturing where precision, safety, and ergonomics are critical. BMW explicitly positions humanoid robotics as a complement to existing automation, not a replacement for workers, aiming to “relieve employees and further improve working conditions” in repetitive or physically taxing roles.

BMW’s production chief Milan Nedeljković, set to become CEO in May, emphasized that the symbiosis of engineering expertise and artificial intelligence opens up entirely new possibilities in production. BMW calls this “Physical AI”—intelligent systems that perceive, reason, and act within complex, real-world manufacturing environments. According to Michael Ströbel, BMW’s head of process management, humanoids could eventually allow BMW to bring work in-house that currently goes to suppliers.

The Leipzig pilot builds on lessons from Spartanburg, South Carolina, where Figure AI’s Figure 02 robot worked daily ten-hour shifts for ten months in 2025, supporting production of over 30,000 BMW X3s. The robot handled precise removal and positioning of sheet metal parts for welding, moving over 90,000 components, logging roughly 1,250 operating hours, and covering approximately 1.2 million steps. The transition from lab to production floor happened faster than expected, with motion sequences transferring smoothly into stable shift operation.

Read more here!

BMW deployed first humanoid robots in Europe!

Physical AI startup RLWRLD raises $26M! 🤖

RLWRLD, a physical AI startup developing robot foundation models for industrial environments, has raised $26 million in Seed 2 funding. This brings the Seoul-based company’s total seed capital to $42 million, following its $14.8 million Seed 1 round in April 2025. The round was led by global venture firm Headline Asia and Z Ventures (the corporate venture capital arm of Z Holdings and Yahoo Japan–LINE), with participation from strategic investors including CJ Logistics, Kakao Investment, Lotte Ventures, Hanwha Asset Management, and Mirae Asset–Emart Investment Fund.

Unlike robotics systems trained primarily in clean, controlled lab settings, RLWRLD’s models are trained using high-precision, multimodal data collected directly inside real factories, warehouses, and service environments. The company exposes its models to the unpredictability of live operations, crowded spaces, variable lighting, human interaction, and imperfect conditions, that are difficult to replicate in simulations. RLWRLD’s scalable, multi-sensor system captures complex demonstrations from diverse industrial settings, generating rich data for robust, adaptable models.

RLWRLD is executing proof-of-concept and robotics transformation projects with enterprise partners across logistics, retail, and service environments in Japan and South Korea. Several initiatives with major companies, including CJ Logistics and Lotte, have progressed from memoranda of understanding (MOUs) into joint industrial validation phases. The company is actively working with multiple strategic investors on joint projects that include using real-world operational data to train and validate its models.

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Wayve raises $1.2B with plans to bring robotaxis to London! 🚖

Wayve Technologies has closed a Series D round of $1.2 billion, bringing its post-money valuation to $8.6 billion. The London-based company plans to use the funding to accelerate commercialization of its end-to-end AI platform for autonomous driving.

Founded in 2017, Wayve’s AI Driver doesn’t rely on deterministic, map-based systems and runs entirely on onboard vehicle compute. The unified AI platform spans SAE Level 2 “hands-off” operations through Level 4 “eyes-off” driving across vehicles, brands, and markets. The company’s AV2.0 foundation model replaces the “sense-plan-act” architecture with a single neural network, trained on data from over 70 countries and a wide range of vehicles for easier generalization to new markets.

Wayve claims to be the first and only AV developer to drive zero-shot in more than 500 cities across Europe, North America, and Japan, meaning without city-specific fine-tuning before deployment. The company licenses its software directly to automakers, providing tools to customize driving models for specific vehicles and brands. By partnering with manufacturers and mobility platforms rather than vertically integrating, Wayve asserts its approach enables autonomous vehicle technology to scale globally with lower capital intensity.

This year, Wayve plans to conduct robotaxi trials with Uber (a past and current investor) in London. Under the partnership, Wayve will deploy its AI Driver in L4-capable vehicles from participating automakers, while Uber will own and operate the fleet. In 2027, consumers will be able to buy AI Driver-equipped passenger vehicles, starting with L2+ capability that allows the vehicle to steer, navigate, and respond to traffic under driver supervision.

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Robotics maps #5 (Odense 🇩🇰)

Odense is one of Europe’s strongest robotics ecosystems, especially for collaborative and industrial robots. At the center is Syddansk Universitet (University of Southern Denmark/SDU), which has a leading robotics research department and works closely with startups. Many founders and engineers come directly from SDU labs, making it easy to turn research into real companies.

A major boost came from the success and acquisitions of Universal Robots and Mobile Industrial Robots. Their global growth and later acquisitions created serial founders and angel investors, capital recycled back into new startups, and a strong global reputation for Odense robotics. Startup support initiatives like Odense Robotics help with global commercialization and partnerships, allowing more robotics startups to be incubated locally with support from experienced mentors who have already built and scaled global robot companies.

Key Odense-based companies include Universal Robots (collaborative robotic arms, acquired by Teradyne for ~$285M), Mobile Industrial Robots/MiR (autonomous mobile robots for internal logistics, acquired by Teradyne for ~$148M), and Teradyne Robotics (Teradyne’s robotics division). OnRobot provides plug-and-play end-of-arm tooling for collaborative robots (~$55M+ raised), while ROICO develops robotic solutions for industrial painting applications.

Other notable players include Spin Robotics (collaborative robot torque tools for automated screwdriving), Enabled Robotics (mobile manipulator robots for flexible logistics automation), Nord Modules (modular top modules for AMRs to expand logistics functionality), and Smooth Robotics (automated welding solutions for cobots). Essential Robotics builds autonomous healthcare solutions, XiniX AI develops AI software and vision for robotics, ARIS Robotics creates advanced robotic systems for industrial and research applications, RobSub builds underwater robotic systems and ROVs for marine applications, and Nordbo Robotics develops force-torque sensor technology and adaptive control software for robots.

Unitree Robotics revealed their new robot dog! 🐕

Chinese robotics firm Unitree Robotics has unveiled the As2, a high-performance quadruped robot built for speed, payload strength, and advanced autonomous capabilities. Weighing about 18 kg (40 pounds) with its battery, the robot delivers up to 90 N·m of maximum joint torque and can reach running speeds above 5 meters per second (approximately 11 mph).

The As2 is powered by a 648Wh, 15,000mAh lithium battery that allows more than four hours of runtime when unloaded, covering distances beyond 20 km (12.4 miles). Even when carrying a 33 lbs load, it maintains more than 2.5 hours of operation with a walking range exceeding 13 km (8.08 miles). Designed for real-world environments, the robot carries an IP54 rating for dust and rain resistance and can operate in temperatures from -20°C (-4°F) to 50°C (122°F).

The quadruped demonstrates impressive mobility metrics. It can climb 25 cm stairs, handle slopes up to 40 degrees, and mount vertical platforms as high as 50 cm. Maximum continuous walking load reaches around 15 kg (33 pounds), while its standing payload capacity goes up to 65 kg (143 pounds) in the EDU variant. Speed varies by model: the As2 AIR runs between 0 and 3.0 m/s, while the PRO and EDU versions reach 3.7 m/s, with peak speeds approaching 5 m/s under no-load conditions.

Mechanically, the As2 features 12 degrees of freedom driven by low-inertia, high-speed inner rotor PMSM motors with industrial-grade crossed roller bearings for high precision and load capacity. The torque-to-weight ratio stands at approximately 5 N·m/kg, enabling powerful yet agile motion. The robot integrates dual encoders for each joint, local air cooling, supply voltage from 36V to 50.4V, plus Wi-Fi 6, Bluetooth 5.2, speaker, microphone, front light, and HD camera across all versions.

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Newest addition to Unitree Robotics family