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𝗖𝗵𝗶𝗻𝗮 𝗶𝘀 𝗳𝗶𝗻𝗶𝘀𝗵𝗶𝗻𝗴 𝘁𝗵𝗲 𝗵𝘂𝗺𝗮𝗻𝗼𝗶𝗱 𝗿𝗼𝗯𝗼𝘁 𝗿𝗮𝗰𝗲 𝗯𝗲𝗳𝗼𝗿𝗲 𝗺𝗼𝘀𝘁 𝗼𝗳 𝘁𝗵𝗲 𝗪𝗲𝘀𝘁 𝗿𝗲𝗮𝗹𝗶𝘇𝗲𝘀 𝗶𝘁 𝗵𝗮𝘀 𝘀𝘁𝗮𝗿𝘁𝗲𝗱. AGIBOT held its Partner Conference in Shanghai last week. The real headline wasn't the new hardware. It was their CTO standing on stage, telling investors that humanoid R&D season is over. 2026, he... said, is "Deployment Year One." Not research. Not demos. Deployment into real factories, real warehouses, real stores. The manufacturing ramp is getting faster. 1,000 humanoid robots in the first 2 years. Another 4,000 in the next 12 months. Another 5,000 in just 3 months after that. AGIBOT is now shipping more humanoids per quarter than most US robotics companies have built in their entire existence. Then came the announcements the industry will spend the rest of the year reacting to. AIMA. The first full-stack open architecture for embodied AI. A unified robot operating system called Link-U, three dev platforms for motion, interaction, and task creation, plus an open agent framework. Any developer can build on top of it. This is the Android play for humanoids. GO-2. A vision-language-action foundation model with Action Chain-of-Thought reasoning. Planning and execution collapsed into one model. GE-2. A world model for simulation, strategy testing, and sim-to-real transfer. AGIBOT WORLD 2026. An open-source, production-grade real-world dataset pulled from actual industrial, logistics, hotel, and commercial sites. Seven standardized "productivity packages" covering logistics sorting, retail service, security patrol, commercial cleaning, and more. Plug, deploy, bill. A 5-year, $280 million commitment to seed a global developer and partner ecosystem. Now look at the competition. Boston Dynamics has been building humanoids since 1992. Tesla's Optimus is still climbing its own hype curve. Apptronik and Agility are well-funded but pre-scale on real deployments. AGIBOT has pulled all of this off in three years, with no acquisitions, no legacy platform, and no IPO distractions. While the West is still asking when humanoids will scale, China is already shipping them by the thousand.show more

Shruti

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214,939 views • 3 months ago •via X (Twitter)

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The First Manufacturer to Mass-Produce 5,000 Humanoid Robots AgiBOT rolled off its 5,000th humanoid robot (a Lingxi X2) today, marking a milestone for both the company and the humanoid robot industry as a whole: a significant step towards large-scale deployment in the real world. AgiBOT co-founder and CTO Peng Zhihui stated in a live said that these 5,000 units include the Yuanzheng series (1742), the Lingxi series (1846), and the Genie series (1412). These humanoid robots will be deployed in various settings, including scientific research, entertainment, shopping malls, factories, and warehouses. This also signals that AgiBOT has the capacity for mass production and is ready to meet future surges in customer demand. Its production partner, LingyiTech, was the main force behind the assembly of AgiBOT's 5,000 humanoid robots this year, and they are planning an automated production line with an annual capacity of 500,000 units. Next year will be a milestone year for humanoid robots, reaching 10,000 units annually. Including Tesla, 1X, UBTECH…

The First Manufacturer to Mass-Produce 5,000 Humanoid Robots AgiBOT rolled off its 5,000th humanoid robot (a Lingxi X2) today, marking a milestone for both the company and the humanoid robot industry as a whole: a significant step towards large-scale deployment in the real world. AgiBOT co-founder and CTO Peng Zhihui stated in a live said that these 5,000 units include the Yuanzheng series (1742), the Lingxi series (1846), and the Genie series (1412). These humanoid robots will be deployed in various settings, including scientific research, entertainment, shopping malls, factories, and warehouses. This also signals that AgiBOT has the capacity for mass production and is ready to meet future surges in customer demand. Its production partner, LingyiTech, was the main force behind the assembly of AgiBOT's 5,000 humanoid robots this year, and they are planning an automated production line with an annual capacity of 500,000 units. Next year will be a milestone year for humanoid robots, reaching 10,000 units annually. Including Tesla, 1X, UBTECH…

CyberRobo

47,095 views • 8 months ago

🔥 JUST IN: Open-source robotics dataset from 100% real-world scenarios! 🤯 Chinese robotics company AGIBOT just released AGIBOT WORLD 2026, an open-source dataset systematically covering key embodied AI research directions. Built entirely from real-world environments: commercial spaces, and homes. Collected using AGIBOT G2 robots in free-form collection mode, providing structured, accurately annotated, high-quality data. Digital twin technology creates 1:1 scale replicas in simulation matching the real environments. Both real-world and simulation data are open-sourced. The AGIBOT G2 platform collects multiple data types simultaneously: RGB(D) cameras, tactile sensors, force sensors, LiDAR, IMU, and full-body joint states. Whole-body control coordinates arms, waist, and hands for complex tasks. First-person teleoperation lets operators control the robot from its perspective. The tasks covered are fine-grained manipulation, ultra-long-horizon tasks, spatial navigation, dual-arm coordination, and multi-agent/human-robot collaboration. The dataset includes error-recovery trajectories with annotations. Most datasets only show successful demonstrations. AGIBOT includes failures and how the robot recovers, teaching models how to handle mistakes. After collection, data is tested through policy training and real-robot deployment to ensure quality. Then processed through industrial quality control with multiple screening and cleaning rounds. Making it open-source accelerates embodied AI research by giving researchers access to high-quality real-world robot data at scale. 🇨🇳 Learn more here: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

🔥 JUST IN: Open-source robotics dataset from 100% real-world scenarios! 🤯 Chinese robotics company AGIBOT just released AGIBOT WORLD 2026, an open-source dataset systematically covering key embodied AI research directions. Built entirely from real-world environments: commercial spaces, and homes. Collected using AGIBOT G2 robots in free-form collection mode, providing structured, accurately annotated, high-quality data. Digital twin technology creates 1:1 scale replicas in simulation matching the real environments. Both real-world and simulation data are open-sourced. The AGIBOT G2 platform collects multiple data types simultaneously: RGB(D) cameras, tactile sensors, force sensors, LiDAR, IMU, and full-body joint states. Whole-body control coordinates arms, waist, and hands for complex tasks. First-person teleoperation lets operators control the robot from its perspective. The tasks covered are fine-grained manipulation, ultra-long-horizon tasks, spatial navigation, dual-arm coordination, and multi-agent/human-robot collaboration. The dataset includes error-recovery trajectories with annotations. Most datasets only show successful demonstrations. AGIBOT includes failures and how the robot recovers, teaching models how to handle mistakes. After collection, data is tested through policy training and real-robot deployment to ensure quality. Then processed through industrial quality control with multiple screening and cleaning rounds. Making it open-source accelerates embodied AI research by giving researchers access to high-quality real-world robot data at scale. 🇨🇳 Learn more here: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

40,583 views • 4 months ago

Another win for open-source robotics! 🔥 Hugging Face just released a fully open-source humanoid robot, and you can build one for $2,500. I'm a huge advocate of open-source in robotics space. Why? Robotics is too hard to solve alone. So lowering the entry point is crucial. Also, ROS proved open ecosystems create lasting industry standards. So let's have a look at the new member of humanoids family! LeRobot Humanoid is a 3D-printed, bipedal humanoid platform designed for real robot learning experiments. It's a complete and, buildable robot Anddddd yes, it's a full stack release: → Hardware files, bill of materials and full assembly documentation → Simulation assets and training environments → Runtime tools for calibration and real-world control → Sim-to-real identification pipeline When a part breaks, reprint it. When a design choice doesn't work, modify it and test again. The whole point is fast iteration on real hardware, not treating the robot as a fixed artefact. The timing is not a coincidence. As foundation models for robotics mature, the field desperately needs open physical platforms to train, validate and deploy them on. clem 🤗 LET'S GO!!! Build it here: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Another win for open-source robotics! 🔥 Hugging Face just released a fully open-source humanoid robot, and you can build one for $2,500. I'm a huge advocate of open-source in robotics space. Why? Robotics is too hard to solve alone. So lowering the entry point is crucial. Also, ROS proved open ecosystems create lasting industry standards. So let's have a look at the new member of humanoids family! LeRobot Humanoid is a 3D-printed, bipedal humanoid platform designed for real robot learning experiments. It's a complete and, buildable robot Anddddd yes, it's a full stack release: → Hardware files, bill of materials and full assembly documentation → Simulation assets and training environments → Runtime tools for calibration and real-world control → Sim-to-real identification pipeline When a part breaks, reprint it. When a design choice doesn't work, modify it and test again. The whole point is fast iteration on real hardware, not treating the robot as a fixed artefact. The timing is not a coincidence. As foundation models for robotics mature, the field desperately needs open physical platforms to train, validate and deploy them on. clem 🤗 LET'S GO!!! Build it here: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

20,361 views • 2 months ago

China's humanoid robotics market is on fire. With orders expected to top 30,000 units this year—a tenfold jump from 2024's total of less than 3,000—2025 is officially shaping up to be the "Year of Mass Production." This surge, driven by an expansion into new sectors like industrial manufacturing, logistics, and elder care, is reflected in a wave of new deals across the industry. Here's a look at some of the key commercial progress: Astribot: A 1,000-unit order for industrial and logistics deployment over two years. TianTai Robotics: Signed a major 10,000-unit order for caregiving robots. Noetix Robotics : Received over 2,000 intent orders in one month, valued at over 100 million yuan, with a focus on education and commercial performances. AgiBot: Expects to ship thousands of units this year and tens of thousands in 2026. Unitree Robotics: Has orders for thousands of units and is one of the most visible products in the industry. UBTech: Aims to deliver 500 industrial humanoids in 2025, with educational robot orders already exceeding 300 units. Robot Era: Delivered over 300 units by July 2025 with 500 more on hand. TLIBOT: Has around 1,000 intent orders. Galbot: Secured orders for its supermarket security robot, Galbot, in 100 stores. AI² Robotics: Has nearly 500 orders for its general-purpose robots for industrial and public service scenarios. But here’s the crucial reality check. While the order boom is exciting, it doesn't automatically translate to fulfilled deliveries. Many companies lack the production capacity to keep up. A significant portion of these are "intent orders" or framework agreements, not guaranteed sales. Furthermore, the market is heavily B2B-focused, with consumer demand representing only about 5% of sales. Some orders are even symbolic, for public relations or strategic purposes. This “order frenzy” is a starting point, not the finish line. The true test for China's humanoid robot industry isn't who can secure the biggest order, but who can consistently deliver on it and build a stable market for the future.

China's humanoid robotics market is on fire. With orders expected to top 30,000 units this year—a tenfold jump from 2024's total of less than 3,000—2025 is officially shaping up to be the "Year of Mass Production." This surge, driven by an expansion into new sectors like industrial manufacturing, logistics, and elder care, is reflected in a wave of new deals across the industry. Here's a look at some of the key commercial progress: Astribot: A 1,000-unit order for industrial and logistics deployment over two years. TianTai Robotics: Signed a major 10,000-unit order for caregiving robots. Noetix Robotics : Received over 2,000 intent orders in one month, valued at over 100 million yuan, with a focus on education and commercial performances. AgiBot: Expects to ship thousands of units this year and tens of thousands in 2026. Unitree Robotics: Has orders for thousands of units and is one of the most visible products in the industry. UBTech: Aims to deliver 500 industrial humanoids in 2025, with educational robot orders already exceeding 300 units. Robot Era: Delivered over 300 units by July 2025 with 500 more on hand. TLIBOT: Has around 1,000 intent orders. Galbot: Secured orders for its supermarket security robot, Galbot, in 100 stores. AI² Robotics: Has nearly 500 orders for its general-purpose robots for industrial and public service scenarios. But here’s the crucial reality check. While the order boom is exciting, it doesn't automatically translate to fulfilled deliveries. Many companies lack the production capacity to keep up. A significant portion of these are "intent orders" or framework agreements, not guaranteed sales. Furthermore, the market is heavily B2B-focused, with consumer demand representing only about 5% of sales. Some orders are even symbolic, for public relations or strategic purposes. This “order frenzy” is a starting point, not the finish line. The true test for China's humanoid robot industry isn't who can secure the biggest order, but who can consistently deliver on it and build a stable market for the future.

RoboHub🤖

199,146 views • 11 months ago

China is leading in the humanoid race. 🇨🇳 UBTECH Robotics has officially shipped hundreds of its Walker S2 humanoids, not to demos or showcases, but directly into factories and logistics centers. Of course, as is often the case in our robotic world, there were doubts and accusations that it was CGI. This is one of the first large-scale industrial rollouts anywhere in the world. The robots are going to partners like BYD, Geely Auto, FAW-Volkswagen, Dongfeng Liuzhou, and Foxconn, where they’ll be used in frontline production tasks. Let's look at monehh: so UBTECH’s 2025 order book now exceeds 800M yuan (~$113M), and the company targets 500 units delivered by year-end. The Walker S2 is built for continuous operation, including an autonomous battery-swap system that lets it replace its own power pack and keep working without downtime. While Europe is still debating standards and long-term frameworks, China is already doing full production, testing, and integration at scale. 🇪🇺 In the meantime, USA claims that Chinese robots are fake, and China casually keep shipping them. 🇺🇸 The challenge is no longer whether humanoids will be used in industry, it’s who gets them deployed, reliable, and affordable first. And right now, Asia is the one shipping robots by the truckload. ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

China is leading in the humanoid race. 🇨🇳 UBTECH Robotics has officially shipped hundreds of its Walker S2 humanoids, not to demos or showcases, but directly into factories and logistics centers. Of course, as is often the case in our robotic world, there were doubts and accusations that it was CGI. This is one of the first large-scale industrial rollouts anywhere in the world. The robots are going to partners like BYD, Geely Auto, FAW-Volkswagen, Dongfeng Liuzhou, and Foxconn, where they’ll be used in frontline production tasks. Let's look at monehh: so UBTECH’s 2025 order book now exceeds 800M yuan (~$113M), and the company targets 500 units delivered by year-end. The Walker S2 is built for continuous operation, including an autonomous battery-swap system that lets it replace its own power pack and keep working without downtime. While Europe is still debating standards and long-term frameworks, China is already doing full production, testing, and integration at scale. 🇪🇺 In the meantime, USA claims that Chinese robots are fake, and China casually keep shipping them. 🇺🇸 The challenge is no longer whether humanoids will be used in industry, it’s who gets them deployed, reliable, and affordable first. And right now, Asia is the one shipping robots by the truckload. ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

13,890 views • 8 months ago

Last night, China Central Television (CCTV) aired its 2026 Chinese New Year Gala celebrating the Year of the Horse. The show featured a wide range of performances, including Unitree Robotics humanoid robots performing martial arts in sync with human dancers. Just a year ago, Unitree’s robots appeared at the same gala, but their movements looked stiff and mechanical. This year, they were noticeably more fluid and coordinated — a remarkable improvement, even if they’re still likely operating under some level of remote supervision. When it comes to humanoid robotics, most of the visible momentum today seems to be coming from the U.S. and China. Companies like Tesla (with Optimus) and Boston Dynamics in the U.S., alongside rapidly advancing Chinese firms, dominate the headlines. So what happened to Europe and Japan? Japan was once seen as the global leader, especially with Honda’s ASIMO and SoftBank Robotics’ humanoid projects. However, ASIMO was retired, and much of Japan’s robotics focus shifted toward industrial automation and service robots rather than full-scale general-purpose humanoids. Europe, meanwhile, remains strong in industrial robotics, research, and precision engineering — with players like ABB and KUKA — but hasn’t pushed aggressively into commercial humanoid platforms at the same scale or speed as the U.S. and China. In short, it’s less that Europe and Japan disappeared, and more that the center of gravity in humanoid robotics — especially AI-driven, general-purpose humanoids — has shifted toward U.S.–China competition. Whether that gap widens or narrows will depend on breakthroughs in embodied AI, cost reduction, and real-world deployment over the next few years.

Last night, China Central Television (CCTV) aired its 2026 Chinese New Year Gala celebrating the Year of the Horse. The show featured a wide range of performances, including Unitree Robotics humanoid robots performing martial arts in sync with human dancers. Just a year ago, Unitree’s robots appeared at the same gala, but their movements looked stiff and mechanical. This year, they were noticeably more fluid and coordinated — a remarkable improvement, even if they’re still likely operating under some level of remote supervision. When it comes to humanoid robotics, most of the visible momentum today seems to be coming from the U.S. and China. Companies like Tesla (with Optimus) and Boston Dynamics in the U.S., alongside rapidly advancing Chinese firms, dominate the headlines. So what happened to Europe and Japan? Japan was once seen as the global leader, especially with Honda’s ASIMO and SoftBank Robotics’ humanoid projects. However, ASIMO was retired, and much of Japan’s robotics focus shifted toward industrial automation and service robots rather than full-scale general-purpose humanoids. Europe, meanwhile, remains strong in industrial robotics, research, and precision engineering — with players like ABB and KUKA — but hasn’t pushed aggressively into commercial humanoid platforms at the same scale or speed as the U.S. and China. In short, it’s less that Europe and Japan disappeared, and more that the center of gravity in humanoid robotics — especially AI-driven, general-purpose humanoids — has shifted toward U.S.–China competition. Whether that gap widens or narrows will depend on breakthroughs in embodied AI, cost reduction, and real-world deployment over the next few years.

Ray

23,439 views • 5 months ago

Elon says China's an Ass-kicker-next-level Question on Q4 earnings call: "There's a surge of Chinese companies humanoids; what is Tesla's long-term competitive advantage?" Elon: "By far, the biggest competition for humanoid robots will be from China. China is good at manufacturing, and also in AI, judging from open models. To the best of my knowledge, we don't see significant competition outside of China. People outside of China underestimate China, but China's an ass-kicker next-level. We think we will be more capable than any robot under development in China. We'll be ahead in real-world intelligence and the electromechanical dexterity of the hand design. There are three hardest things for humanoid robots: - the hand - real-world AI - and scaling production Tesla is the only company that has all three of those components."

Elon says China's an Ass-kicker-next-level Question on Q4 earnings call: "There's a surge of Chinese companies humanoids; what is Tesla's long-term competitive advantage?" Elon: "By far, the biggest competition for humanoid robots will be from China. China is good at manufacturing, and also in AI, judging from open models. To the best of my knowledge, we don't see significant competition outside of China. People outside of China underestimate China, but China's an ass-kicker next-level. We think we will be more capable than any robot under development in China. We'll be ahead in real-world intelligence and the electromechanical dexterity of the hand design. There are three hardest things for humanoid robots: - the hand - real-world AI - and scaling production Tesla is the only company that has all three of those components."

The Humanoid Hub

114,545 views • 6 months ago

$Most robotics teams can make a robot move… very few can make it work every day in the real world! @roboforce_inc was just featured in Jensen Huang’s GTC keynote, not for a demo but for a robot they claim is already working in the field. Their system, TITAN, is built for industrial environments: ✅ 40 kg dual-arm payload ✅ millimeter-level precision ✅ 8 hours runtime ✅ early pilots in energy, logistics, and manufacturing They call it Physical AI: The focus is on getting robots to do the kind of labor that still depends on people: repetitive, risky, and physical. IMO, what stands out isn’t the specs. It’s the claim of 11,000 robot orders❗️ If even a fraction of that turns into real deployments, it would make RoboForce one of the most widely scaled robot platforms in the world!!?? The open question is whether this shift from prototype to production is real or just the next phase of robotics storytelling. It looks very promising to me. It’s also interesting how their founder openly pushes back against the humanoid trend, arguing for wheels over legs in real industrial settings. It’s an unusual perspective in a time when humanoids get most of the attention. If they are right, the next humanoid robotics wave might look less like a human and more like an industrial version. Seen at —- Weekly robotics and AI insights. Subscribe free:$

Most robotics teams can make a robot move… very few can make it work every day in the real world! @roboforce_inc was just featured in Jensen Huang’s GTC keynote, not for a demo but for a robot they claim is already working in the field. Their system, TITAN, is built for industrial environments: ✅ 40 kg dual-arm payload ✅ millimeter-level precision ✅ 8 hours runtime ✅ early pilots in energy, logistics, and manufacturing They call it Physical AI: The focus is on getting robots to do the kind of labor that still depends on people: repetitive, risky, and physical. IMO, what stands out isn’t the specs. It’s the claim of 11,000 robot orders❗️ If even a fraction of that turns into real deployments, it would make RoboForce one of the most widely scaled robot platforms in the world!!?? The open question is whether this shift from prototype to production is real or just the next phase of robotics storytelling. It looks very promising to me. It’s also interesting how their founder openly pushes back against the humanoid trend, arguing for wheels over legs in real industrial settings. It’s an unusual perspective in a time when humanoids get most of the attention. If they are right, the next humanoid robotics wave might look less like a human and more like an industrial version. Seen at —- Weekly robotics and AI insights. Subscribe free:

Ilir Aliu

27,741 views • 9 months ago

Skild has been quiet since emerging from stealth in July 2024. They just shared their journey so far, showcasing early milestones. Skild is building toward a single general-purpose, “omni-bodied” robotics foundation model: One Brain – any task, any robot. The Skild Brain can control humanoids, quadrupeds, or manipulators using a unified architecture trained on large-scale action datasets, including internet videos, simulation, and real-world data. Founded by CMU professors in 2023, the company has a strong group of researchers and engineers who have made some of the most seminal contributions to robotics research in the past decade. Now, they’re set to take it from the lab to the real world.

Skild has been quiet since emerging from stealth in July 2024. They just shared their journey so far, showcasing early milestones. Skild is building toward a single general-purpose, “omni-bodied” robotics foundation model: One Brain – any task, any robot. The Skild Brain can control humanoids, quadrupeds, or manipulators using a unified architecture trained on large-scale action datasets, including internet videos, simulation, and real-world data. Founded by CMU professors in 2023, the company has a strong group of researchers and engineers who have made some of the most seminal contributions to robotics research in the past decade. Now, they’re set to take it from the lab to the real world.

The Humanoid Hub

62,424 views • 1 year ago

JUST IN: Unitree Robotics just unveiled the GD01, a manned "mecha" robot that transforms between two legs and four legs in real time. This is not a concept. This is a product. Starting price: $574,000. This robot weighs 500kg, is built from high-strength alloy, and has a torso-mounted cockpit. The demo video shows it walking upright, knocking down a brick wall with its hand, then reconfiguring into a four-legged crawl. Unitree calls it the world's first mass-produced transformable mecha. But the real story here is the bigger picture. Chinese companies accounted for nearly 90% of global humanoid robot sales in 2025. Unitree alone shipped 5,500+ humanoid robots last year. In the same period, Tesla, Figure AI and Agility Robotics each shipped around 150. And Unitree is now filing for an IPO on Shanghai's Star Market, targeting 4.2 billion yuan, 85% of which goes straight into R&D. The West is building incredible humanoid robots. China is building them faster, cheaper and at a scale nobody else is close to matching. Read more here: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

JUST IN: Unitree Robotics just unveiled the GD01, a manned "mecha" robot that transforms between two legs and four legs in real time. This is not a concept. This is a product. Starting price: $574,000. This robot weighs 500kg, is built from high-strength alloy, and has a torso-mounted cockpit. The demo video shows it walking upright, knocking down a brick wall with its hand, then reconfiguring into a four-legged crawl. Unitree calls it the world's first mass-produced transformable mecha. But the real story here is the bigger picture. Chinese companies accounted for nearly 90% of global humanoid robot sales in 2025. Unitree alone shipped 5,500+ humanoid robots last year. In the same period, Tesla, Figure AI and Agility Robotics each shipped around 150. And Unitree is now filing for an IPO on Shanghai's Star Market, targeting 4.2 billion yuan, 85% of which goes straight into R&D. The West is building incredible humanoid robots. China is building them faster, cheaper and at a scale nobody else is close to matching. Read more here: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

11,580 views • 2 months ago

For robots to be useful, they must be able to interact with a wide variety of environments; and yet, scaling interaction data is difficult, expensive, and time consuming. Instead, much research revolves around sim-to-real manipulation — but mostly this has not been mobile manipulation. Recently, though, this has begun to change. Two recent papers from Tairan He and Haoru Xue show us how to unlock the potential of this technique, building policies which, without any real data at all, can move objects around in the world and open doors in the real world with a humanoid robot. Watch Episode #60 of RoboPapers now to learn more, hosted by Chris Paxton and Jiafei Duan. In this episode, we cover two papers:. First is VIRAL: Visual Sim-to-Real at Scale for Humanoid Loco-Manipulation; and second is DoorMan: Opening the Sim-to-Real Door for Humanoid Pixel-to-Action Policy Transfer.

For robots to be useful, they must be able to interact with a wide variety of environments; and yet, scaling interaction data is difficult, expensive, and time consuming. Instead, much research revolves around sim-to-real manipulation — but mostly this has not been mobile manipulation. Recently, though, this has begun to change. Two recent papers from Tairan He and Haoru Xue show us how to unlock the potential of this technique, building policies which, without any real data at all, can move objects around in the world and open doors in the real world with a humanoid robot. Watch Episode #60 of RoboPapers now to learn more, hosted by Chris Paxton and Jiafei Duan. In this episode, we cover two papers:. First is VIRAL: Visual Sim-to-Real at Scale for Humanoid Loco-Manipulation; and second is DoorMan: Opening the Sim-to-Real Door for Humanoid Pixel-to-Action Policy Transfer.

RoboPapers

30,767 views • 6 months ago

Chinese robotics company Astribot released their latest World-Action Model (WAM), Lumo-2. Technical breakdown: - based on a frozen 🥶 Qwen-3.5 4B VLM - trained in 3 progressive stages: 1. Action is aligned with latent world dynamics (an abstract representation of action). Real-world actions are anchored to physical constraints, while the latent space is guided to focus on motion-relevant changes. This bidirectional relationship makes the model physically grounded -> critical for a world model. 2. Action is aligned with vision and language. Reusing the vision backbone and action encoder from the frozen VLM, the authors add a custom vocabulary (for new actions), a semantic module, an action decoder, and an action projector. This aligns the (new) action representations with the (existing) vision-language semantic space. Most importantly: it builds a direct mapping from natural-language instructions to motor execution. 3. End-to-end training on language, video, and robot data. Only the new modules (everything outside the frozen backbone) are trained end-to-end across temporal reasoning, physical understanding, long-horizon, and dexterous manipulation. At the end of the day, Lumo-2 is not the best on benchmarks, but that's not the point. What's genuinely new: - a way to combine latent world modeling and action generation through progressive alignment - a physically-grounded latent dynamics space - it lifts performance on unseen objects using un-annotated human egocentric video + Vision Pro captures, no special transfer algorithm needed Why it matters: - the whole model is thin trainable adapters (semantic module, action decoder/projector) on a frozen 4B backbone (cheap) - that scale is suited for real-time embedded inference (~2.71× decode speedup, no accuracy loss) - its real moat is long-horizon execution, where the added temporal memory pays off far more than on any other task As a result, this robot can now make your latte (5x sped up video):

Chinese robotics company Astribot released their latest World-Action Model (WAM), Lumo-2. Technical breakdown: - based on a frozen 🥶 Qwen-3.5 4B VLM - trained in 3 progressive stages: 1. Action is aligned with latent world dynamics (an abstract representation of action). Real-world actions are anchored to physical constraints, while the latent space is guided to focus on motion-relevant changes. This bidirectional relationship makes the model physically grounded -> critical for a world model. 2. Action is aligned with vision and language. Reusing the vision backbone and action encoder from the frozen VLM, the authors add a custom vocabulary (for new actions), a semantic module, an action decoder, and an action projector. This aligns the (new) action representations with the (existing) vision-language semantic space. Most importantly: it builds a direct mapping from natural-language instructions to motor execution. 3. End-to-end training on language, video, and robot data. Only the new modules (everything outside the frozen backbone) are trained end-to-end across temporal reasoning, physical understanding, long-horizon, and dexterous manipulation. At the end of the day, Lumo-2 is not the best on benchmarks, but that's not the point. What's genuinely new: - a way to combine latent world modeling and action generation through progressive alignment - a physically-grounded latent dynamics space - it lifts performance on unseen objects using un-annotated human egocentric video + Vision Pro captures, no special transfer algorithm needed Why it matters: - the whole model is thin trainable adapters (semantic module, action decoder/projector) on a frozen 4B backbone (cheap) - that scale is suited for real-time embedded inference (~2.71× decode speedup, no accuracy loss) - its real moat is long-horizon execution, where the added temporal memory pays off far more than on any other task As a result, this robot can now make your latte (5x sped up video):

Léo

32,078 views • 16 days ago

The Beijing Innovation Center of Humanoid Robotics (X-Humanoid) is building its own "data factory" to tackle the biggest bottleneck in Embodied AI, aiming for a world-first 1-million-hour milestone of high-quality real-world data. 🤖 This 53,820-square-foot (5,000 square meters) facility is already operating with a fleet of over 120 robots from various makers, including their own Tien Kung series and platforms like Unitree and UBTECH. By replicating 30+ real-life scenarios—from kitchens to industrial lines—the center is capturing the "physical intuition" that digital simulations simply cannot replicate. Their "factory-style" pipeline ensures a 95% data pass rate through a rigorous three-stage inspection process, moving beyond the era of small-scale, "fine-tuned" lab datasets. With over 2 million downloads of their open-source Robomind dataset, they are quickly becoming the backbone for developers looking to bridge the sim-to-real gap.

The Beijing Innovation Center of Humanoid Robotics (X-Humanoid) is building its own "data factory" to tackle the biggest bottleneck in Embodied AI, aiming for a world-first 1-million-hour milestone of high-quality real-world data. 🤖 This 53,820-square-foot (5,000 square meters) facility is already operating with a fleet of over 120 robots from various makers, including their own Tien Kung series and platforms like Unitree and UBTECH. By replicating 30+ real-life scenarios—from kitchens to industrial lines—the center is capturing the "physical intuition" that digital simulations simply cannot replicate. Their "factory-style" pipeline ensures a 95% data pass rate through a rigorous three-stage inspection process, moving beyond the era of small-scale, "fine-tuned" lab datasets. With over 2 million downloads of their open-source Robomind dataset, they are quickly becoming the backbone for developers looking to bridge the sim-to-real gap.

RoboHub🤖

23,072 views • 4 months ago

Today Reactor is coming out of stealth. We’ve raised $59M in Seed and Series A funding, led by Lightspeed, with participation from Amplify Partners, wndrco, Sky9 Capital, and FPV Ventures. Reactor is the platform for building in the World Model era: the infrastructure that lets developers build with them at global scale for the first time. Stream from a frontier World Model to your app, in real time, all in under 10 lines of code. World Models represent the next major shift in AI: pixels, audio and actions are generated on the fly, in real-time, in response to user inputs, and to the environment. Every time computing has made a shift from passive to interactive, entire industries appeared that didn't exist before. We're standing in front of such moment again. Over the last 6 months, we’ve assembled an all-star team with alumni from Apple, Meta, Google, Luma AI, Netflix, and Replicate. We're already partnering with some of the biggest names and labs in the world, and hundreds of developers are already building on Reactor. The World Model era starts now.

Today Reactor is coming out of stealth. We’ve raised $59M in Seed and Series A funding, led by Lightspeed, with participation from Amplify Partners, wndrco, Sky9 Capital, and FPV Ventures. Reactor is the platform for building in the World Model era: the infrastructure that lets developers build with them at global scale for the first time. Stream from a frontier World Model to your app, in real time, all in under 10 lines of code. World Models represent the next major shift in AI: pixels, audio and actions are generated on the fly, in real-time, in response to user inputs, and to the environment. Every time computing has made a shift from passive to interactive, entire industries appeared that didn't exist before. We're standing in front of such moment again. Over the last 6 months, we’ve assembled an all-star team with alumni from Apple, Meta, Google, Luma AI, Netflix, and Replicate. We're already partnering with some of the biggest names and labs in the world, and hundreds of developers are already building on Reactor. The World Model era starts now.

reactor

12,735,559 views • 2 months ago

Finally. After 18 months of patience, FSD is officially approved and live in the Netherlands. I’ve been driving with it for 2 days now. And the experience is on another level. It feels like the future unfolding in real time. The precision. The intelligence. The confidence of the system. This is not incremental progress. This is a clear step forward in how mobility works. Huge thanks to Elon Musk and the entire AI team at Tesla. From a professional perspective, this shows what is possible when software, data, and real-world deployment come together at scale. The question is no longer if this will take over. The question is how fast it will expand. This is a turning point. Elon Musk

Finally. After 18 months of patience, FSD is officially approved and live in the Netherlands. I’ve been driving with it for 2 days now. And the experience is on another level. It feels like the future unfolding in real time. The precision. The intelligence. The confidence of the system. This is not incremental progress. This is a clear step forward in how mobility works. Huge thanks to Elon Musk and the entire AI team at Tesla. From a professional perspective, this shows what is possible when software, data, and real-world deployment come together at scale. The question is no longer if this will take over. The question is how fast it will expand. This is a turning point. Elon Musk

NL F$D Inside

25,129,509 views • 3 months ago

AI for robotics has some monumental challenges, like high dimensionality and reasoning with the physical world. Ashok Elluswamy, AI lead at Tesla, thinks the hardest problem is Evaluation. - Loss isn't a perfect indicator of Policy Neural Network's quality. - Testing in the real world for all the long-tail failure cases is not possible. To solve the evaluation challenge, Tesla has developed a World Simulator Model. It is trained on state and action pairs, so for a given current camera feed and current action, the model generates the next frame in the cameras. Once the world simulation is good enough, it can be hooked up to the policy network for a closed-loop simulation. In this setup, the model generates the next video frames, and based on those, the policy network comes up with the next action, which feeds back into the world simulation; this continuous loop creates a coherent simulation. The world simulation neural network is not just applicable to self-driving but also to humanoids, because Tesla trains it on common data. The examples show Optimus navigating or manipulating inside the world simulation, with all the pixels being generated by the world simulation model. The robotic agents can be tested in an accurately reproduced edge case and in other potential variations of the same edge case.

AI for robotics has some monumental challenges, like high dimensionality and reasoning with the physical world. Ashok Elluswamy, AI lead at Tesla, thinks the hardest problem is Evaluation. - Loss isn't a perfect indicator of Policy Neural Network's quality. - Testing in the real world for all the long-tail failure cases is not possible. To solve the evaluation challenge, Tesla has developed a World Simulator Model. It is trained on state and action pairs, so for a given current camera feed and current action, the model generates the next frame in the cameras. Once the world simulation is good enough, it can be hooked up to the policy network for a closed-loop simulation. In this setup, the model generates the next video frames, and based on those, the policy network comes up with the next action, which feeds back into the world simulation; this continuous loop creates a coherent simulation. The world simulation neural network is not just applicable to self-driving but also to humanoids, because Tesla trains it on common data. The examples show Optimus navigating or manipulating inside the world simulation, with all the pixels being generated by the world simulation model. The robotic agents can be tested in an accurately reproduced edge case and in other potential variations of the same edge case.

The Humanoid Hub

17,065 views • 6 months ago

AheadForm just raised a new A1 round worth hundreds of millions of RMB (~tens of millions USD) 🤖 That matters because this is not another humanoid company chasing locomotion first. AheadForm is building around the part most robotics startups still underestimate: face, emotion, and real-time human connection. The new funding will go into multimodal embodied interaction, emotion foundation models, facial hardware and materials, standardized delivery, and global expansion. Founded in June 2024, the company is still young, but the founder’s research trail is not. Yuhang Hu, a Columbia PhD and AheadForm’s CEO/CTO, has published work spanning facial coexpression, realistic lip motion for humanoid face robots, and self-supervised robot self-modeling. That is the deeper signal here. In a market crowded with hands, arms, and walking demos, investors are now putting serious money behind embodied AI that can express, respond, and hold attention face to face. And the company is moving fast. According to public reports, AheadForm has completed five funding rounds since the second half of 2025, while its robots have already broken out of lab-only visibility through public activations like the NetEase Justice mobile game collaboration and large robot-stage appearances. If humanoid robotics is about physical labor, AheadForm is making the case that the next layer is emotional presence. That may end up being one of the more important categories in embodied AI.

AheadForm just raised a new A1 round worth hundreds of millions of RMB (~tens of millions USD) 🤖 That matters because this is not another humanoid company chasing locomotion first. AheadForm is building around the part most robotics startups still underestimate: face, emotion, and real-time human connection. The new funding will go into multimodal embodied interaction, emotion foundation models, facial hardware and materials, standardized delivery, and global expansion. Founded in June 2024, the company is still young, but the founder’s research trail is not. Yuhang Hu, a Columbia PhD and AheadForm’s CEO/CTO, has published work spanning facial coexpression, realistic lip motion for humanoid face robots, and self-supervised robot self-modeling. That is the deeper signal here. In a market crowded with hands, arms, and walking demos, investors are now putting serious money behind embodied AI that can express, respond, and hold attention face to face. And the company is moving fast. According to public reports, AheadForm has completed five funding rounds since the second half of 2025, while its robots have already broken out of lab-only visibility through public activations like the NetEase Justice mobile game collaboration and large robot-stage appearances. If humanoid robotics is about physical labor, AheadForm is making the case that the next layer is emotional presence. That may end up being one of the more important categories in embodied AI.

RoboHub🤖

155,548 views • 4 months ago

Turning video into humanoid robot motion! 🤳🏼 Training humanoid robots needs huge amounts of motion data, but real-world capture doesn’t scale. Mocap is expensive, dangerous edge cases are rare, and you can’t ask humans to repeatedly fall or crash. Video2Robot tackles this by converting videos into physics-grounded humanoid simulations. Motion is generated to respect balance, inertia, ground contact, and joint limits, then directly retargeted to robot simulators. One prompt can generate a full humanoid motion sequence, including multi-agent interactions and failure cases like falls or collisions, scenarios that are hard or impossible to capture safely in the real world. The pipeline is model-agnostic and works with existing video generators, making it a practical way to scale data for robots. If robots are going to operate in the real world, they need to be trained on the failures too, not just the perfect demos. Here's the GitHub: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Turning video into humanoid robot motion! 🤳🏼 Training humanoid robots needs huge amounts of motion data, but real-world capture doesn’t scale. Mocap is expensive, dangerous edge cases are rare, and you can’t ask humans to repeatedly fall or crash. Video2Robot tackles this by converting videos into physics-grounded humanoid simulations. Motion is generated to respect balance, inertia, ground contact, and joint limits, then directly retargeted to robot simulators. One prompt can generate a full humanoid motion sequence, including multi-agent interactions and failure cases like falls or collisions, scenarios that are hard or impossible to capture safely in the real world. The pipeline is model-agnostic and works with existing video generators, making it a practical way to scale data for robots. If robots are going to operate in the real world, they need to be trained on the failures too, not just the perfect demos. Here's the GitHub: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

Lukas Ziegler

59,835 views • 7 months ago

A robot just worked an 8-hour shift in China. Then it handled an error no human flagged, adapting to a conveyor that shifted under it and correcting the position deviation on its own before carrying on. This is AgiBot's G2 at Longcheer in Nanchang, where the fleet has now run 10 hours straight and sorted more than 3,000 units at a 100% success rate, talking to the factory's own software in real time and flagging defective parts by itself. We built every factory on earth around human limits: the eight-hour shift, the breaks, the way attention slips after hour six. AgiBot is making those limits optional, and the setting is the whole point. This is a live production line shipping real product next to human workers, not a staged demo. Figure ran a similar stream in California last month, so the milestone itself isn't unique. What's different here is that nothing is for the camera. A functioning factory, live, no cuts. I got back from China this month, and the pace of this one ecosystem is the thing the US is underrating most. The Terminator version of this was loud and apocalyptic. The real one just shows up to work, and gets a little better every shift.

A robot just worked an 8-hour shift in China. Then it handled an error no human flagged, adapting to a conveyor that shifted under it and correcting the position deviation on its own before carrying on. This is AgiBot's G2 at Longcheer in Nanchang, where the fleet has now run 10 hours straight and sorted more than 3,000 units at a 100% success rate, talking to the factory's own software in real time and flagging defective parts by itself. We built every factory on earth around human limits: the eight-hour shift, the breaks, the way attention slips after hour six. AgiBot is making those limits optional, and the setting is the whole point. This is a live production line shipping real product next to human workers, not a staged demo. Figure ran a similar stream in California last month, so the milestone itself isn't unique. What's different here is that nothing is for the camera. A functioning factory, live, no cuts. I got back from China this month, and the pace of this one ecosystem is the thing the US is underrating most. The Terminator version of this was loud and apocalyptic. The real one just shows up to work, and gets a little better every shift.

Shruti

11,014 views • 1 month ago

X Square Robot just closed its Series C at a valuation above RMB 20 billion, about $2.8 billion 🤖 IDG came into this round. The bigger signal is the cap table. HongShan and Xiaomi were already in across earlier rounds, while Meituan, Alibaba, ByteDance, and Xiaomi have each led rounds at different stages. That puts X Square in a rare position for an embodied AI company: top-tier financial capital on one side, and four of China’s biggest tech platforms on the other. This is not just a money story. Meituan, Alibaba, ByteDance, and Xiaomi bring very different strategic assets: real-world scenarios, cloud infrastructure, consumer traffic, supply chains, and hardware ecosystems. The deployment side is already moving: robot home-cleaning services first, then a “Robots Into Homes” program with the first batch entering real households. The model stack is worth watching too. X Square has open-sourced WALL-OSS-0.5 for robot manipulation and WALL-WM for world modeling. WALL-OSS-0.5 showed strong real-robot performance without post-training, while WALL-WM uses event-level prediction to align language, vision, and action around meaningful physical-world events. They are also building a model-driven data pipeline for large-scale collection, cleaning, annotation, quality control, and augmentation. That matters because home robotics dies in the long tail: weird rooms, messy objects, bad lighting, and tasks that never look the same twice. Founded in 2023, X Square is building general-purpose embodied AI robots and foundation models for real-world environments, tying models, robot hardware, high-precision manipulation, data, and deployment into one system.

X Square Robot just closed its Series C at a valuation above RMB 20 billion, about $2.8 billion 🤖 IDG came into this round. The bigger signal is the cap table. HongShan and Xiaomi were already in across earlier rounds, while Meituan, Alibaba, ByteDance, and Xiaomi have each led rounds at different stages. That puts X Square in a rare position for an embodied AI company: top-tier financial capital on one side, and four of China’s biggest tech platforms on the other. This is not just a money story. Meituan, Alibaba, ByteDance, and Xiaomi bring very different strategic assets: real-world scenarios, cloud infrastructure, consumer traffic, supply chains, and hardware ecosystems. The deployment side is already moving: robot home-cleaning services first, then a “Robots Into Homes” program with the first batch entering real households. The model stack is worth watching too. X Square has open-sourced WALL-OSS-0.5 for robot manipulation and WALL-WM for world modeling. WALL-OSS-0.5 showed strong real-robot performance without post-training, while WALL-WM uses event-level prediction to align language, vision, and action around meaningful physical-world events. They are also building a model-driven data pipeline for large-scale collection, cleaning, annotation, quality control, and augmentation. That matters because home robotics dies in the long tail: weird rooms, messy objects, bad lighting, and tasks that never look the same twice. Founded in 2023, X Square is building general-purpose embodied AI robots and foundation models for real-world environments, tying models, robot hardware, high-precision manipulation, data, and deployment into one system.

RoboHub🤖

12,975 views • 1 month ago