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.One Robot builds world model-based simulations that are realistic to see and realistic to interact with, so robotics teams can train and evaluate VLAs without being bottlenecked by robot time. Congrats on the launch, Hemanth Sarabu and Elton Bear!

Y Combinator

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25,940 次观看 • 6 个月前 •via X (Twitter)

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So cool! Can you even tell this is a robot horse? 🤖🐎 DEEP Robotics has built a robot horse, seemingly a special Year of the Horse limited edition based on their M20 Pro. The bionic details on this machine are incredible, especially those lifelike hooves. It looks like a living creature rather than just another quadruped robot. It is amazing to see a quad robot move so smoothly while having such a realistic animal appearance. Source: DEEP Robotics #Robot #Quadruped #Robotics #AI #EmbodiedAI #PhysicalAI #DEEPRobotics #Bionics #TechDesign

So cool! Can you even tell this is a robot horse? 🤖🐎 DEEP Robotics has built a robot horse, seemingly a special Year of the Horse limited edition based on their M20 Pro. The bionic details on this machine are incredible, especially those lifelike hooves. It looks like a living creature rather than just another quadruped robot. It is amazing to see a quad robot move so smoothly while having such a realistic animal appearance. Source: DEEP Robotics #Robot #Quadruped #Robotics #AI #EmbodiedAI #PhysicalAI #DEEPRobotics #Bionics #TechDesign

RoboHub🤖

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Train robots. Earn rewards. Base is pioneering the Train-to-Earn model for robotics. With Axis Robotics, anyone can help train robot simulations from a laptop and be rewarded for contributing data. The future of robotics won’t be trained by a handful of companies. It’ll be trained by everyone. PL 漂亮 (✱,✱) showcased this vision at SuperAI, the largest AI event in Asia 👇

Train robots. Earn rewards. Base is pioneering the Train-to-Earn model for robotics. With Axis Robotics, anyone can help train robot simulations from a laptop and be rewarded for contributing data. The future of robotics won’t be trained by a handful of companies. It’ll be trained by everyone. PL 漂亮 (✱,✱) showcased this vision at SuperAI, the largest AI event in Asia 👇

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Unitree Robotics modified its G1 humanoid robot by replacing its feet with wheels, roller skates, and ice skates. This allows the robot to move faster and more smoothly on different surfaces. The upgrade shows how one robot can adapt to multiple types of movement with simple changes.

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Tired of teleoperation? One human video → 1,000s of robot demos. (📍GitHub ) Scaling Robot Data Without Dynamics Simulation or Robot Hardware Real2Render2Real (R2R2R) is a new way to scale robot data without physics simulation or hardware. You take a phone scan + a single monocular human demo. It tracks the motion, renders photorealistic scenes, and generates diverse, robot-agnostic trajectories ready for training. > No teleop, no sim, no robot, just a phone and a video > Train VLA models and diffusion policies directly on the output > Supports multiple robot embodiments with kinematic consistency > 1000s of demos in 1/27 the time of real-world collection Thank you, Max Fu, for sharing!! Project: Paper: Code coming soon: It shows that with the right pipeline, you can scale robot learning data without touching a robot. One of the most interesting directions in scalable robotics today. —— Weekly robotics and AI insights. Subscribe free:

Tired of teleoperation? One human video → 1,000s of robot demos. (📍GitHub ) Scaling Robot Data Without Dynamics Simulation or Robot Hardware Real2Render2Real (R2R2R) is a new way to scale robot data without physics simulation or hardware. You take a phone scan + a single monocular human demo. It tracks the motion, renders photorealistic scenes, and generates diverse, robot-agnostic trajectories ready for training. > No teleop, no sim, no robot, just a phone and a video > Train VLA models and diffusion policies directly on the output > Supports multiple robot embodiments with kinematic consistency > 1000s of demos in 1/27 the time of real-world collection Thank you, Max Fu, for sharing!! Project: Paper: Code coming soon: It shows that with the right pipeline, you can scale robot learning data without touching a robot. One of the most interesting directions in scalable robotics today. —— Weekly robotics and AI insights. Subscribe free:

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Can robot foundation models scale? Xiaomi-Robotics-1 explores this question with over 100,000 hours of real-world manipulation data. Pre-trained on large-scale real-world trajectories and post-trained with cross-embodiment robot data, Xiaomi-Robotics-1 demonstrates consistent scaling across data and model size, strong generalization in unseen environments, and efficient adaptation to new tasks. 🔗 #Robotics #EmbodiedAI #FoundationModels #RobotLearning #XiaomiAI #XiaomiRobotics

Can robot foundation models scale? Xiaomi-Robotics-1 explores this question with over 100,000 hours of real-world manipulation data. Pre-trained on large-scale real-world trajectories and post-trained with cross-embodiment robot data, Xiaomi-Robotics-1 demonstrates consistent scaling across data and model size, strong generalization in unseen environments, and efficient adaptation to new tasks. 🔗 #Robotics #EmbodiedAI #FoundationModels #RobotLearning #XiaomiAI #XiaomiRobotics

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Meet Sprout, a small humanoid robot developed by Fauna Robotics for research, education, and social interaction. It can walk, climb stairs, and safely interact with people with its lightweight design and soft exterior. Fauna Robotics was acquired by Amazon, and Sprout continues to serve as a platform for robotics research and development.

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PrimeBot, a Chinese robotics company, has unveiled the world's first transformable robot, T1. It can switch on its own between a two-wheeled humanoid mode for indoor movement and a four-legged mode for walking on grass or climbing stairs. The robot can also interact by voice, understand its surroundings, and remember routines and spaces to respond more naturally over time. PrimeBOT_Global

Space and Technology

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.General Astronautics builds autonomous robotics for space. In microgravity, proteins crystallize without defects and semiconductors form without flaws, but astronaut time is scarce. They are enabling scale for the work that can't be done anywhere else. Congrats on the launch, Bram Schork and Shibo Zhou!

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YC F24's Rulebase builds AI-driven fraud detection models to prevent account takeovers and voice phishing for fintechs and banks. They also run voice phishing simulations to train employees against bad actors. Congrats on the launch, Gideon + chidi!

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Y Combinator

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15–30 minutes of real-world robot data. That's now enough to go from sim-to-real failure to working robot. Let’s see… You train a robot in simulation. You deploy it in the real world. It fails. The physics don't match. So you try to fine-tune it with real data, but… you never have enough real data, and the fine-tuning breaks everything the simulation taught it. SimDist fixes this with one key decision: don't transfer the policy. Transfer the world model. Keep the reward and value knowledge from simulation frozen. Only update the part that's actually wrong, how the robot predicts physics. Now the robot doesn't have to relearn the entire task in the real world. It already knows what success looks like. It just needs to correct its understanding of how the real world moves. The part that makes this work: they also trained on failures and recoveries; not just perfect demonstrations. Without that, the planner finds the gaps and exploits them. With it, the robot can tell a good future from a bad one. That's all it needs. Results on peg insertion, table leg assembly, locomotion on slippery and uneven surfaces. Tasks that require precision, force, and quick reaction. Thanks for sharing, Tyler Westenbroek ([Tyler Westenbroek]. Interactive visualization + paper: ——- Weekly robotics and AI insights. Subscribe free:

15–30 minutes of real-world robot data. That's now enough to go from sim-to-real failure to working robot. Let’s see… You train a robot in simulation. You deploy it in the real world. It fails. The physics don't match. So you try to fine-tune it with real data, but… you never have enough real data, and the fine-tuning breaks everything the simulation taught it. SimDist fixes this with one key decision: don't transfer the policy. Transfer the world model. Keep the reward and value knowledge from simulation frozen. Only update the part that's actually wrong, how the robot predicts physics. Now the robot doesn't have to relearn the entire task in the real world. It already knows what success looks like. It just needs to correct its understanding of how the real world moves. The part that makes this work: they also trained on failures and recoveries; not just perfect demonstrations. Without that, the planner finds the gaps and exploits them. With it, the robot can tell a good future from a bad one. That's all it needs. Results on peg insertion, table leg assembly, locomotion on slippery and uneven surfaces. Tasks that require precision, force, and quick reaction. Thanks for sharing, Tyler Westenbroek ([Tyler Westenbroek]. Interactive visualization + paper: ——- Weekly robotics and AI insights. Subscribe free:

Ilir Aliu

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A humanoid robot policy trained solely on synthetic data generated by a world model. Research Scientist Joel Jang presents NVIDIA's DreamGen pipeline: ⦿ Post-train the world model Cosmos-Predict2 with a small set of real teleoperation demos. ⦿ Prompt the world model to generate synthetic video data with verbs and scenarios not used in the world model’s post-training. ⦿ Auto-label synthetic video data with action sequences. ⦿ Train robot policies using only synthetic data. That's it. Deploy zero-shot to a real humanoid robot.

The Humanoid Hub

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A good latte is part art, part science. So can a robot achieve that delicate balance? It can if it’s powered by Intel Robotics, Intel Core Ultra Series 3, and the Robotics AI Suite. Through a combination of real-time control and AI acceleration on a single chip, Ella, a robot inside an autonomous store experience, has the accuracy and deterministic control to serve up the perfect cup every time. See Ella for yourself at the Sensory AI coffee stand at the Taipei World Trade Center Entrance Hall during COMPUTEX 2026.

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A robotic system developed by US-based defence technology company Onyx Industries can travel both on land and across water. It combines the Naut mobility platform with the Sentry defence system on the Ghost Robotics V60 robot. The robot is designed for surveillance, protection, and intelligence missions.

Space and Technology

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X Square Robot has introduced TwinDEX: a co-designed pair of three-finger, 9-DoF dexterous hands, one wearable for data collection, one matched robot end effector for deployment. The wearable and the robot hand share the same kinematics, geometry, materials, look, and sensors, so human demos map straight to the robot without retargeting. Human data collection improves throughput 5.3x compared to teleoperation, and a policy can be trained with a few hundred episodes without any robot data.

X Square Robot has introduced TwinDEX: a co-designed pair of three-finger, 9-DoF dexterous hands, one wearable for data collection, one matched robot end effector for deployment. The wearable and the robot hand share the same kinematics, geometry, materials, look, and sensors, so human demos map straight to the robot without retargeting. Human data collection improves throughput 5.3x compared to teleoperation, and a policy can be trained with a few hundred episodes without any robot data.

The Humanoid Hub

277,022 次观看 • 19 天前

Testing robot policies on hardware is slow, expensive and hard to scale. World models offer a promising path to accelerating robot policy development. We're sharing new research from the Runway Robotics team, in which we simulated 8 robot policies inside our General World Model and found 0.95 correlation with real-world results. Those early results point to world model simulation as a practical substitute for hardware evaluation, comparing favorably to existing real-to-sim approaches. Learn more at the link below.

Testing robot policies on hardware is slow, expensive and hard to scale. World models offer a promising path to accelerating robot policy development. We're sharing new research from the Runway Robotics team, in which we simulated 8 robot policies inside our General World Model and found 0.95 correlation with real-world results. Those early results point to world model simulation as a practical substitute for hardware evaluation, comparing favorably to existing real-to-sim approaches. Learn more at the link below.

Runway

13,464 次观看 • 6 个月前