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🚀Top embodied intelligence competition is back! AGIBOT WORLD CHALLENGE @ ICRA 2026 Reasoning-Action & World Model tracks $530K prize pool Robot finals at ICRA 2026 Win AGIBOT robot purchase vouchers Global teams competing Apply now!

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AGIBOT just dominated its debut at the 2nd World Humanoid Robot Games in Beijing. The company finished #1 in both the gold medal table and the overall medal table: • 18 gold medals • 16 silver medals • 12 bronze medals • 46 medals total What stands out is the hardware behind those results. AGIBOT says its competition lineup, including OmniHand, G2, A3 and X2, consists of robots already in mass production or deployed in real-world applications, rather than machines built only for competition. Some of the strongest results: • OmniHand reached all 8 dexterous-hand finals and won 7 gold medals, including Block Building, Powder Weighing and Bean Picking with Tweezers. • AGIBOT A3 won gold in Tai Chi, testing whole-body coordination, balance and controlled posture transitions. • AGIBOT X2 took gold in the obstacle race. • AGIBOT also won 5 gold medals across scenario-based tasks including hotel services, library operations and emergency response. • AGIBOT G2, used during the Games, has already been deployed in factories operated by companies including Longcheer and SAIC. AGIBOT also announced in June 2026 that its 15,000th robot had rolled off the production line. The World Humanoid Robot Games are becoming much more than a showcase of running and dancing robots. Dexterous manipulation, industrial tasks, service work and emergency-response scenarios are increasingly part of the competition. For AGIBOT, 46 medals provide another public test of the same robotic platforms it is trying to move from demonstrations into real-world deployment.

AGIBOT just dominated its debut at the 2nd World Humanoid Robot Games in Beijing. The company finished #1 in both the gold medal table and the overall medal table: • 18 gold medals • 16 silver medals • 12 bronze medals • 46 medals total What stands out is the hardware behind those results. AGIBOT says its competition lineup, including OmniHand, G2, A3 and X2, consists of robots already in mass production or deployed in real-world applications, rather than machines built only for competition. Some of the strongest results: • OmniHand reached all 8 dexterous-hand finals and won 7 gold medals, including Block Building, Powder Weighing and Bean Picking with Tweezers. • AGIBOT A3 won gold in Tai Chi, testing whole-body coordination, balance and controlled posture transitions. • AGIBOT X2 took gold in the obstacle race. • AGIBOT also won 5 gold medals across scenario-based tasks including hotel services, library operations and emergency response. • AGIBOT G2, used during the Games, has already been deployed in factories operated by companies including Longcheer and SAIC. AGIBOT also announced in June 2026 that its 15,000th robot had rolled off the production line. The World Humanoid Robot Games are becoming much more than a showcase of running and dancing robots. Dexterous manipulation, industrial tasks, service work and emergency-response scenarios are increasingly part of the competition. For AGIBOT, 46 medals provide another public test of the same robotic platforms it is trying to move from demonstrations into real-world deployment.

Techniahqrobot | humanoid robots

14,869 views • 23 days 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 • 5 months ago

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.

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.

Shruti

214,939 views • 4 months ago

At the 2026 World Robot Conference, we saw China's robots in action, from welding cars on factory lines to sorting packages in warehouses, from making coffee to sensing a feather landing on a robotic palm. They're not perfect yet. The real challenge ahead is making these systems reliable, flexible and useful beyond the lab or the show floor. #WRC #robot #dexteroushands #roboticarms

At the 2026 World Robot Conference, we saw China's robots in action, from welding cars on factory lines to sorting packages in warehouses, from making coffee to sensing a feather landing on a robotic palm. They're not perfect yet. The real challenge ahead is making these systems reliable, flexible and useful beyond the lab or the show floor. #WRC #robot #dexteroushands #roboticarms

China Xinhua News

12,418 views • 29 days ago

I placed 🥈 2nd in the LeHome Challenge (at IEEE ICRA 2026) earlier this month, and before that I was 🥇 1st of 62 teams in the simulation round. Now I am sharing my solution — with a detailed logic walkthrough and open-source code. The task was to teach a cheap two-armed robot to fold different garments in simulation and on a real robot. I trained a VLA policy with an RL loop to make it work. Let's break it down 👇

I placed 🥈 2nd in the LeHome Challenge (at IEEE ICRA 2026) earlier this month, and before that I was 🥇 1st of 62 teams in the simulation round. Now I am sharing my solution — with a detailed logic walkthrough and open-source code. The task was to teach a cheap two-armed robot to fold different garments in simulation and on a real robot. I trained a VLA policy with an RL loop to make it work. Let's break it down 👇

Ilia

21,948 views • 2 months ago

Xiaomi’s humanoid robot at the 2026 World Robot Conference. 🤖🌹 On Chinese Valentine’s Day, the robot is part of the flower-gifting experience on the show floor. From picking a flower and adding decorative confetti to handing it to a visitor, it handles the entire sequence autonomously. Powered by an embodied model, it completes the interaction without remote human teleoperation. Note: Developed as a non-commercial prototype for internal use at Xiaomi EV Factory. Not for sale. #HumanoidRobots #EmbodiedAI #Robotics #XiaomiRobotics #WRC2026

Xiaomi’s humanoid robot at the 2026 World Robot Conference. 🤖🌹 On Chinese Valentine’s Day, the robot is part of the flower-gifting experience on the show floor. From picking a flower and adding decorative confetti to handing it to a visitor, it handles the entire sequence autonomously. Powered by an embodied model, it completes the interaction without remote human teleoperation. Note: Developed as a non-commercial prototype for internal use at Xiaomi EV Factory. Not for sale. #HumanoidRobots #EmbodiedAI #Robotics #XiaomiRobotics #WRC2026

Xiaomi Tech

20,474 views • 1 month ago

This robot knows who won the World Cup! "Xiaozhi, Xiaozhi, who won the 2026 FIFA World Cup?" Alvaro Lago, from Spain, gave this "pop quiz" to a robot at the Embodied AI Robot Experience Center in Changzhou, east China's Jiangsu Province. "The 2026 World Cup Champions are Spain!" the robot blurted out instantly, drawing an enthusiastic thumbs-up from the Spanish native. From understanding football to offering services and interacting with people, what's on display is more than just robots, it's a showcase of China's manufacturing strength. The experience center brings together 36 companies and 104 exhibits, spanning core components, intelligent systems and complete machine integration, offering a full panorama of the embodied intelligence industry chain. The Wujin National Hi-tech Industrial Zone, site of the experience center, is already home to more than 120 robotics companies and is accelerating efforts to build a globally competitive robotics industry hub. As China's robots race toward the future, one thing is clear: when it comes to robotics, Changzhou is a city you don't want to miss.

This robot knows who won the World Cup! "Xiaozhi, Xiaozhi, who won the 2026 FIFA World Cup?" Alvaro Lago, from Spain, gave this "pop quiz" to a robot at the Embodied AI Robot Experience Center in Changzhou, east China's Jiangsu Province. "The 2026 World Cup Champions are Spain!" the robot blurted out instantly, drawing an enthusiastic thumbs-up from the Spanish native. From understanding football to offering services and interacting with people, what's on display is more than just robots, it's a showcase of China's manufacturing strength. The experience center brings together 36 companies and 104 exhibits, spanning core components, intelligent systems and complete machine integration, offering a full panorama of the embodied intelligence industry chain. The Wujin National Hi-tech Industrial Zone, site of the experience center, is already home to more than 120 robotics companies and is accelerating efforts to build a globally competitive robotics industry hub. As China's robots race toward the future, one thing is clear: when it comes to robotics, Changzhou is a city you don't want to miss.

People's Daily, China

28,786 views • 1 month ago

TARS has made some big announcements at ICRA 2026. DexHand - A dexterous hand with 1:1 replication of human metacarpal and finger structure, multi-axis thumb opposition - Quasi-direct-drive + backdrivable joints, 21 degrees of freedom - All 26 ASL letter signs reproduced at high precision -0.05mm fingertip tactile sensing - Data. Model. Hand. Fused into one embodied AI-native dexterous hand. OmniVTA with TacForeSight A visuo-tactile world model for general-use, paired with TacForeSight, a force-guided technology that not only allows robots to understand contact but also enables them to predict contact before it happens. - Semantic touch ("slippery," "rough," "soft," "hard") enabling texture-based material cognition. - Operates inside a real-time closed loop - Contact state prediction and reaction at the microsecond scale, faster than human AWE 3.0 - Claimed to be the world's leading general-purpose embodied foundation model for real-world work - A1 robot packing demo as the showcase: long-horizon, bimanual, with autonomous error recovery and end-to-end imitation learning - Set a Guinness World Record in March 2026 for "Most sub-millimeter wire harness assembled by a robot in one hour" - SenseHub data acquisition suite captures human experience data to train AWE 3.0

TARS has made some big announcements at ICRA 2026. DexHand - A dexterous hand with 1:1 replication of human metacarpal and finger structure, multi-axis thumb opposition - Quasi-direct-drive + backdrivable joints, 21 degrees of freedom - All 26 ASL letter signs reproduced at high precision -0.05mm fingertip tactile sensing - Data. Model. Hand. Fused into one embodied AI-native dexterous hand. OmniVTA with TacForeSight A visuo-tactile world model for general-use, paired with TacForeSight, a force-guided technology that not only allows robots to understand contact but also enables them to predict contact before it happens. - Semantic touch ("slippery," "rough," "soft," "hard") enabling texture-based material cognition. - Operates inside a real-time closed loop - Contact state prediction and reaction at the microsecond scale, faster than human AWE 3.0 - Claimed to be the world's leading general-purpose embodied foundation model for real-world work - A1 robot packing demo as the showcase: long-horizon, bimanual, with autonomous error recovery and end-to-end imitation learning - Set a Guinness World Record in March 2026 for "Most sub-millimeter wire harness assembled by a robot in one hour" - SenseHub data acquisition suite captures human experience data to train AWE 3.0

The Humanoid Hub

11,322 views • 3 months ago

A Chinese robot just performed a flying side kick, a sword fight, and a fencing match. with no CGI or pre-programmed scripts. I made a video breaking it down with my honest takeaway… These are Kung Fu moves that take martial artists years to perfect. A machine just did them on camera, in real time, with the same precision as a trained human. The company behind this is called AGIBOT. They're based in Shanghai, and here is the number I keep coming back to…AGIBOT shipped roughly 15,000 humanoid robots in the first half of 2026. That's more than every Western robotics company combined. The AGIBOT A3 Ultra is the commercial version. 700 TOPS of compute. LiDAR, surround-view cameras, dual depth sensors. It sees 360 degrees, positions itself within one centimeter accuracy, and predicts risks three meters ahead. It's designed to work 24/7 in factories, hotels, retail stores, and patrol routes. AGIBOT now controls 44% of the global humanoid robotics market. They're certified in China, the US, and Europe. Their robots can coordinate in swarms of over 100 units at once.

A Chinese robot just performed a flying side kick, a sword fight, and a fencing match. with no CGI or pre-programmed scripts. I made a video breaking it down with my honest takeaway… These are Kung Fu moves that take martial artists years to perfect. A machine just did them on camera, in real time, with the same precision as a trained human. The company behind this is called AGIBOT. They're based in Shanghai, and here is the number I keep coming back to…AGIBOT shipped roughly 15,000 humanoid robots in the first half of 2026. That's more than every Western robotics company combined. The AGIBOT A3 Ultra is the commercial version. 700 TOPS of compute. LiDAR, surround-view cameras, dual depth sensors. It sees 360 degrees, positions itself within one centimeter accuracy, and predicts risks three meters ahead. It's designed to work 24/7 in factories, hotels, retail stores, and patrol routes. AGIBOT now controls 44% of the global humanoid robotics market. They're certified in China, the US, and Europe. Their robots can coordinate in swarms of over 100 units at once.

Shruti

548,282 views • 23 days ago

🏆 Bluwhale Discord World Cup Kicktip Challenge 2026 🏆 Join our official Discord prediction challenge and compete with the community ⚽🌍 🎁 Prize pool worth ~$5,000: • 3x Masternodes • 35x Common Nodes • 13x NFTs • $1,000 in $BLUAI • Top 100 participants receive 1000 XP Participation is FREE. To participate: • Join the Bluwhale Discord: • Reach Discord Level 2 • Complete registration + verification through Discord Good luck to all predictors 🚀

🏆 Bluwhale Discord World Cup Kicktip Challenge 2026 🏆 Join our official Discord prediction challenge and compete with the community ⚽🌍 🎁 Prize pool worth ~$5,000: • 3x Masternodes • 35x Common Nodes • 13x NFTs • $1,000 in $BLUAI • Top 100 participants receive 1000 XP Participation is FREE. To participate: • Join the Bluwhale Discord: • Reach Discord Level 2 • Complete registration + verification through Discord Good luck to all predictors 🚀

Bluwhale

38,480 views • 4 months ago

Galbot has unveiled ET1, its first bipedal humanoid, at WRC 2026. The specs are not revealed, but it looks quite similar to Unitree R1. The robot runs Galbot's world-action model, built to drive bipedal, wheeled, and heavy-load robots from a single brain. Founded in 2023 by Peking University professor He Wang, the company is planning a Hong Kong IPO at ~$3-4B.

Galbot has unveiled ET1, its first bipedal humanoid, at WRC 2026. The specs are not revealed, but it looks quite similar to Unitree R1. The robot runs Galbot's world-action model, built to drive bipedal, wheeled, and heavy-load robots from a single brain. Founded in 2023 by Peking University professor He Wang, the company is planning a Hong Kong IPO at ~$3-4B.

The Humanoid Hub

52,555 views • 1 month ago

🏯 PCI Grand Finals 🏯 Teams from around the world are gathering in Japan for the PCI Tournament Grand Finals this weekend, following several rounds of fierce competition ⚡ Our Finalists will battle it out on the Global Stage for a piece of the massive ~$65k+ Prize Pool...but only one team will walk away with the title of PARAVOX Champion 🥇 Catch our live broadcasts on Saturday, December 21st, and Sunday, December 22nd — follow our official PARAVOX accounts below now! 👇 A new era of esports is upon us 💥

🏯 PCI Grand Finals 🏯 Teams from around the world are gathering in Japan for the PCI Tournament Grand Finals this weekend, following several rounds of fierce competition ⚡ Our Finalists will battle it out on the Global Stage for a piece of the massive ~$65k+ Prize Pool...but only one team will walk away with the title of PARAVOX Champion 🥇 Catch our live broadcasts on Saturday, December 21st, and Sunday, December 22nd — follow our official PARAVOX accounts below now! 👇 A new era of esports is upon us 💥

PARAVOX

47,383 views • 1 year ago

Do you think you could train a robot to fold clothes better than any other robotics team? Now is your chance to prove it...and win actual cash prizes! The 2026 LeHome Challenge is live! Here is the details: Phase 1: Train your best clothes folding policy in simulation with 4 garment types, each presenting unique manipulation challenges. The types of garments include long-sleeved tops, short-sleeved tops, long pants, and shorts. The simulation environment uses Lightwheel's physics accurate deformable assets and built on NVIDIA Omniverse's Isaac Sim/Lab. Submit your code by April 5th for a chance to win up to $2,000 and a SO-101 Arm Kit. Phase 2: The top 8 teams will compete in person at ICRA on June 4th to see who's policy performs best with real clothes. The top place team will take home $20,000! Learn more and sign up for the LeHome Challenge here: Robert Scoble Chris Paxton The Humanoid Hub Harrison Kinsley #Robotics

Do you think you could train a robot to fold clothes better than any other robotics team? Now is your chance to prove it...and win actual cash prizes! The 2026 LeHome Challenge is live! Here is the details: Phase 1: Train your best clothes folding policy in simulation with 4 garment types, each presenting unique manipulation challenges. The types of garments include long-sleeved tops, short-sleeved tops, long pants, and shorts. The simulation environment uses Lightwheel's physics accurate deformable assets and built on NVIDIA Omniverse's Isaac Sim/Lab. Submit your code by April 5th for a chance to win up to $2,000 and a SO-101 Arm Kit. Phase 2: The top 8 teams will compete in person at ICRA on June 4th to see who's policy performs best with real clothes. The top place team will take home $20,000! Learn more and sign up for the LeHome Challenge here: Robert Scoble Chris Paxton The Humanoid Hub Harrison Kinsley #Robotics

Jonathan Stephens

24,003 views • 7 months ago

Boston Dynamics just gave its robot dog a brain that reasons about the physical world. Google DeepMind's Gemini Robotics model is now running inside Spot, the four-legged robot already deployed at thousands of facilities worldwide. The upgrade centers on something called embodied reasoning. Basically, instead of following rigid instructions, Spot now interprets what it sees and makes decisions on its own. One standout example: Spot can now look at a pressure gauge, zoom in on the needle, calculate the intervals between tick marks, and deliver an accurate reading. All autonomously. The model also introduces: > Multi-view reasoning, combining feeds from multiple cameras to understand its environment even when objects are blocked from one angle > Built-in success detection, so Spot knows when it's failed and decides whether to retry or move on That last part is a key requirement for true autonomy. Most companies building AI-powered robots are still stuck in the demo phase. Boston Dynamics is one of the few with actual paying customers... which means thousands of Spots collecting real-world data that feeds back into making the AI smarter. That flywheel is hard to compete with.

Boston Dynamics just gave its robot dog a brain that reasons about the physical world. Google DeepMind's Gemini Robotics model is now running inside Spot, the four-legged robot already deployed at thousands of facilities worldwide. The upgrade centers on something called embodied reasoning. Basically, instead of following rigid instructions, Spot now interprets what it sees and makes decisions on its own. One standout example: Spot can now look at a pressure gauge, zoom in on the needle, calculate the intervals between tick marks, and deliver an accurate reading. All autonomously. The model also introduces: > Multi-view reasoning, combining feeds from multiple cameras to understand its environment even when objects are blocked from one angle > Built-in success detection, so Spot knows when it's failed and decides whether to retry or move on That last part is a key requirement for true autonomy. Most companies building AI-powered robots are still stuck in the demo phase. Boston Dynamics is one of the few with actual paying customers... which means thousands of Spots collecting real-world data that feeds back into making the AI smarter. That flywheel is hard to compete with.

Rowan Cheung

54,376 views • 5 months ago

NEW: China launches its first humanoid robot soccer league in Beijing. This is way more entertaining than regular soccer. The AI-controlled robots were supplied by Booster Robotics for the tournament and have the skills of 5 to 6 year old children. Robots were seen getting carried off the field on stretchers after falling over. Booster Robotics founder Cheng Hao says the robots' abilities will exponentially increase over time and will eventually challenge adult teams. "We chose the football scenario for robot competition primarily for two reasons: first, to encourage students to apply their algorithmic skills to real-world robotics," Cheng said. "Second, to showcase the robots' ability to walk autonomously and stably, withstand collisions, and demonstrate higher levels of intelligence and safety."

NEW: China launches its first humanoid robot soccer league in Beijing. This is way more entertaining than regular soccer. The AI-controlled robots were supplied by Booster Robotics for the tournament and have the skills of 5 to 6 year old children. Robots were seen getting carried off the field on stretchers after falling over. Booster Robotics founder Cheng Hao says the robots' abilities will exponentially increase over time and will eventually challenge adult teams. "We chose the football scenario for robot competition primarily for two reasons: first, to encourage students to apply their algorithmic skills to real-world robotics," Cheng said. "Second, to showcase the robots' ability to walk autonomously and stably, withstand collisions, and demonstrate higher levels of intelligence and safety."

Collin Rugg

10,124,994 views • 1 year 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 • 2 months ago

Beijing hosts world’s first humanoid robot games | Ariana News The world’s inaugural humanoid robot competition is underway in Beijing, drawing more than 500 robots from 280 teams across 16 countries to compete in a uniquely futuristic sporting spectacle. The three-day event, held at the National Speed Skating Oval—once the “Ice Ribbon” of the 2022 Winter Olympics—kicked off on August 15 and runs through to Sunday August 17. The tournament features 26 events spread across athletic, performance, and scenario-based categories. Athletic challenges include sprinting, soccer, and kickboxing, while performance segments showcase robot dance routines and musical instrument displays. Real-world scenarios, such as medication sorting, cleaning tasks, and industrial material handling, are also on the agenda to test practical functionality. Organizers meanwhile emphasize the event’s role in accelerating the integration of humanoid robots into everyday life, from manufacturing and hospitality to healthcare. One Chinese official summed it up: “Every robot that participates is creating history.” The competition has yielded both triumphant strides and technical stumbling blocks. In running events, the robot H1 from Unitree Robotics claimed top honors in the 1,500-meter race, demonstrating promising agility. Yet, many robots struggled with balance, coordination, and task execution, including some collapsing mid-sprint or requiring human help to stand—underscoring the still-developing nature of embodied artificial intelligence.

Beijing hosts world’s first humanoid robot games | Ariana News The world’s inaugural humanoid robot competition is underway in Beijing, drawing more than 500 robots from 280 teams across 16 countries to compete in a uniquely futuristic sporting spectacle. The three-day event, held at the National Speed Skating Oval—once the “Ice Ribbon” of the 2022 Winter Olympics—kicked off on August 15 and runs through to Sunday August 17. The tournament features 26 events spread across athletic, performance, and scenario-based categories. Athletic challenges include sprinting, soccer, and kickboxing, while performance segments showcase robot dance routines and musical instrument displays. Real-world scenarios, such as medication sorting, cleaning tasks, and industrial material handling, are also on the agenda to test practical functionality. Organizers meanwhile emphasize the event’s role in accelerating the integration of humanoid robots into everyday life, from manufacturing and hospitality to healthcare. One Chinese official summed it up: “Every robot that participates is creating history.” The competition has yielded both triumphant strides and technical stumbling blocks. In running events, the robot H1 from Unitree Robotics claimed top honors in the 1,500-meter race, demonstrating promising agility. Yet, many robots struggled with balance, coordination, and task execution, including some collapsing mid-sprint or requiring human help to stand—underscoring the still-developing nature of embodied artificial intelligence.

Owen Gregorian

43,158 views • 1 year 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

32,331 views • 1 month ago