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Not a preplanned motion sequence. A robot deciding mid-jump what to do next. [📍 paper + demo] Researchers just showed a humanoid doing real parkour using only onboard perception. No motion script, no fixed obstacle layout. The system is called Perceptive Humanoid Parkour (PHP). Instead of memorizing a path,... the robot reads depth from its cameras and continuously chooses actions. Step, vault, climb, or roll depending on what geometry appears in front of it. To make that possible, they combine three ideas: First, they stitch together human motion clips into long movement references so the robot learns fluid transitions instead of isolated tricks. Second, they train tracking policies with reinforcement learning so contacts land at the right time and the robot keeps balance during dynamic moves. Finally, everything is distilled into one perception policy that runs directly from depth input to action selection. The result on a Unitree G1: about 3 m/s vaults wall climbs up to 1.25 m nearly one minute continuous obstacle traversal adapting when obstacles move What matters is not the tricks. It is the shift in capability. Earlier humanoids executed motions. This one navigates situations. Once robots react to geometry instead of replaying trajectories, environments stop needing to be predictable. Warehouses, homes, and outdoors suddenly become the same problem. Thanks for sharing, Zhen Wu! Paper + demo: ——— Weekly robotics and AI insights. Subscribe free:show more

Ilir Aliu

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22,080 次观看 • 4 个月前 •via X (Twitter)

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Perceptive Humanoid Parkour (PHP) introduces a modular framework that enables the Unitree G1 humanoid to perform long-horizon, vision-based parkour. - It chains retargeted human motion clips into diverse, long-horizon kinematic reference trajectories. - RL expert policies learn individual skills that are distilled into a depth-conditioned student policy. - The robot autonomously selects the appropriate skill based on the obstacle geometry.

Perceptive Humanoid Parkour (PHP) introduces a modular framework that enables the Unitree G1 humanoid to perform long-horizon, vision-based parkour. - It chains retargeted human motion clips into diverse, long-horizon kinematic reference trajectories. - RL expert policies learn individual skills that are distilled into a depth-conditioned student policy. - The robot autonomously selects the appropriate skill based on the obstacle geometry.

The Humanoid Hub

61,055 次观看 • 4 个月前

This work makes a humanoid robot do simple parkour moves by looking with a depth camera and choosing the right move on the fly. The big deal is that it turns lots of small human moves into long, real-time robot behavior, without hand-coding every transition or retraining for each new course. A humanoid robot is usually good at steady walking, but it often fails when it has to do fast moves like jumping up, vaulting, or rolling, and then keep going to the next obstacle. The hard part is that you cannot easily collect training data for every possible obstacle shape, distance, and mistake, so robots end up learning a few moves that only work in a narrow setup. This work starts from short clips of real human parkour moves, like stepping over, vaulting, climbing, and rolling. It uses motion matching, which is basically a smart “pick the next clip that fits best right now” search, to stitch those short clips into a long, smooth plan that looks like a human doing a whole course. Then it trains a controller with reinforcement learning (RL), which means the robot learns by trial and error to copy that plan while staying balanced and not falling. After training separate expert controllers for different moves, it compresses them into 1 controller that uses only onboard depth sensing and a simple “go this fast in this direction” command. In real tests on a Unitree G1 humanoid, it can clear multiple obstacles in a row, adapt when obstacles get moved, and climb a wall up to 1.25m.

This work makes a humanoid robot do simple parkour moves by looking with a depth camera and choosing the right move on the fly. The big deal is that it turns lots of small human moves into long, real-time robot behavior, without hand-coding every transition or retraining for each new course. A humanoid robot is usually good at steady walking, but it often fails when it has to do fast moves like jumping up, vaulting, or rolling, and then keep going to the next obstacle. The hard part is that you cannot easily collect training data for every possible obstacle shape, distance, and mistake, so robots end up learning a few moves that only work in a narrow setup. This work starts from short clips of real human parkour moves, like stepping over, vaulting, climbing, and rolling. It uses motion matching, which is basically a smart “pick the next clip that fits best right now” search, to stitch those short clips into a long, smooth plan that looks like a human doing a whole course. Then it trains a controller with reinforcement learning (RL), which means the robot learns by trial and error to copy that plan while staying balanced and not falling. After training separate expert controllers for different moves, it compresses them into 1 controller that uses only onboard depth sensing and a simple “go this fast in this direction” command. In real tests on a Unitree G1 humanoid, it can clear multiple obstacles in a row, adapt when obstacles get moved, and climb a wall up to 1.25m.

Rohan Paul

37,121 次观看 • 4 个月前

Push a human off balance and they react instantly. Now try building that into a robot… During a recent visit to Hangzhou, hosted by the Zhejiang Provincial Government, we saw a live balance test from Unitree robotics G1 robot. This is where control, perception, and hardware have to move as one in real time.

Push a human off balance and they react instantly. Now try building that into a robot… During a recent visit to Hangzhou, hosted by the Zhejiang Provincial Government, we saw a live balance test from Unitree robotics G1 robot. This is where control, perception, and hardware have to move as one in real time.

Megatron

43,080 次观看 • 1 个月前

Researchers developed a system called LATENT that teaches humanoid robots to play tennis using small pieces of human movement data. It learns basic motion fragments, then corrects and combines them to create natural movements and swings. The system was tested on the Unitree G1 humanoid robot, enabling it to hit balls and rally with human players.

Researchers developed a system called LATENT that teaches humanoid robots to play tennis using small pieces of human movement data. It learns basic motion fragments, then corrects and combines them to create natural movements and swings. The system was tested on the Unitree G1 humanoid robot, enabling it to hit balls and rally with human players.

Space and Technology

45,667 次观看 • 1 个月前

🚨 Apple joins the robotics race! Researchers from Carnegie Mellon University and Apple have developed a robot collision avoidance system. They called it ARMOR and they implemented it on the Fourier GR-1 robot. What is ARMOR? 👘 ARMOR is a system that helps humanoid robots "see" their surroundings better by using small, smart depth sensors placed on their arms. It solves the problem of blind spots and occlusions in traditional robot perception, making robots much safer and more efficient. It uses Time-of-Flight (ToF) sensors to detect obstacles, providing a near-complete view of the robot’s environment. 🔭 A smart software powered by transformer-based AI learns from human movements to help robots plan their actions dynamically. ARMOR reduces collisions by an impressive 63.7%, making robots significantly better at navigating crowded or complex spaces. The system is fast as well! 💨 ARMOR-Policy cuts computational time by 26× compared to traditional planning methods, ensuring robots can act quickly. P.S. Thoughts on this? The research paper is in the comments! ~~~ ♻️ RT to help 1 robot find a new workplace.

🚨 Apple joins the robotics race! Researchers from Carnegie Mellon University and Apple have developed a robot collision avoidance system. They called it ARMOR and they implemented it on the Fourier GR-1 robot. What is ARMOR? 👘 ARMOR is a system that helps humanoid robots "see" their surroundings better by using small, smart depth sensors placed on their arms. It solves the problem of blind spots and occlusions in traditional robot perception, making robots much safer and more efficient. It uses Time-of-Flight (ToF) sensors to detect obstacles, providing a near-complete view of the robot’s environment. 🔭 A smart software powered by transformer-based AI learns from human movements to help robots plan their actions dynamically. ARMOR reduces collisions by an impressive 63.7%, making robots significantly better at navigating crowded or complex spaces. The system is fast as well! 💨 ARMOR-Policy cuts computational time by 26× compared to traditional planning methods, ensuring robots can act quickly. P.S. Thoughts on this? The research paper is in the comments! ~~~ ♻️ RT to help 1 robot find a new workplace.

Lukas Ziegler

169,366 次观看 • 1 年前

TWIST: a real-time teleoperation system for humanoid robots to mimic whole-body motions. Reference motion data is generated by retargeting human motion-capture data to the robot. Then, the controller is trained in simulation using reinforcement learning and behavior cloning.

TWIST: a real-time teleoperation system for humanoid robots to mimic whole-body motions. Reference motion data is generated by retargeting human motion-capture data to the robot. Then, the controller is trained in simulation using reinforcement learning and behavior cloning.

The Humanoid Hub

46,715 次观看 • 1 年前

Researchers developed Hitter, a humanoid robot that can play table tennis in real-world settings. It was trained on the Unitree G1 robot to predict the ball’s path, choose the right shot, and react in less than a second. In tests, it achieved 106 consecutive shots with a human and even rallied with another humanoid robot.

Researchers developed Hitter, a humanoid robot that can play table tennis in real-world settings. It was trained on the Unitree G1 robot to predict the ball’s path, choose the right shot, and react in less than a second. In tests, it achieved 106 consecutive shots with a human and even rallied with another humanoid robot.

Space and Technology

25,681 次观看 • 2 个月前

VLA policies learn generalist robot behaviors from massive teleoperation datasets, hoping that the right behavior emerges. But they rarely use perception during training or inference: powerful foundation models of 3D geometry, semantics, or human motion are ignored. TimSong, Long Le, and our GRASP Laboratory team introduce Omniguide, based on a simple idea: Instead of retraining policies, let perception guide action at inference time. We express diverse guidance sources as attractive and repulsive energy fields in 3D space and inject their gradients into the generative process of VLA policies. This lets perception modules steer actions without retraining the policy: A modular path toward composable robot intelligence. Project: Paper:

VLA policies learn generalist robot behaviors from massive teleoperation datasets, hoping that the right behavior emerges. But they rarely use perception during training or inference: powerful foundation models of 3D geometry, semantics, or human motion are ignored. TimSong, Long Le, and our GRASP Laboratory team introduce Omniguide, based on a simple idea: Instead of retraining policies, let perception guide action at inference time. We express diverse guidance sources as attractive and repulsive energy fields in 3D space and inject their gradients into the generative process of VLA policies. This lets perception modules steer actions without retraining the policy: A modular path toward composable robot intelligence. Project: Paper:

Kostas Daniilidis

47,324 次观看 • 3 个月前

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

59,693 次观看 • 6 个月前

We got Dagsen a $53,000 humanoid robot from America’s first humanoid robot store, then challenged him to turn it into an autonomous being in just 12 hours. Thank you REK and Dimensional for your help! 00:00 Humanoids are everywhere, how hard are they to use? 00:57 Meeting a robot operating system developer 02:12 What can a Unitree humanoid robot do straight out of the box? 03:25 Visiting America’s first humanoid robot store 04:11 Unpacking the robot 04:50 Taking the robot for a run 05:22 Programming the robot 06:31 Calling in engineering experts 07:15 Speaking Chinese with a robot 07:27 The history of humanoid robots 08:40 A breakthrough 09:09 Robot vs obstacle course

We got Dagsen a $53,000 humanoid robot from America’s first humanoid robot store, then challenged him to turn it into an autonomous being in just 12 hours. Thank you REK and Dimensional for your help! 00:00 Humanoids are everywhere, how hard are they to use? 00:57 Meeting a robot operating system developer 02:12 What can a Unitree humanoid robot do straight out of the box? 03:25 Visiting America’s first humanoid robot store 04:11 Unpacking the robot 04:50 Taking the robot for a run 05:22 Programming the robot 06:31 Calling in engineering experts 07:15 Speaking Chinese with a robot 07:27 The history of humanoid robots 08:40 A breakthrough 09:09 Robot vs obstacle course

Soon

23,340 次观看 • 24 天前

'LATENT' learns tennis skills for humanoid robots from human motion data. The robot can sustain multi-shot rallies, handle ball speeds of 15+ m/s, and showed a 90.9% success rate for the forehand. No onboard cameras or vision models, relies on external MoCap for high-precision, low-latency ball tracking. Paper:

'LATENT' learns tennis skills for humanoid robots from human motion data. The robot can sustain multi-shot rallies, handle ball speeds of 15+ m/s, and showed a 90.9% success rate for the forehand. No onboard cameras or vision models, relies on external MoCap for high-precision, low-latency ball tracking. Paper:

The Humanoid Hub

63,824 次观看 • 3 个月前

Twitter boi, do you want to see a humanoid AI lab from the inside? Well I do ;) Join me to visit Flexion Robotics A world-leading lab building the AI brains for humanoids – in Zurich, one of the best hubs for Physical AI. Their goal? Build the Android of robotics. One operating system that works on any humanoid hardware. It is crazy what their robots can do. We pushed a robot on the stairs. It didn't care. They left one in a rainy forest it was never trained on. Chilled and hiked there. And because they let robots train on their own, from zero through reinforcement learning, the robots do odd inhuman things. Eg they stand up from the ground in ways no human ever would. If their humanoid brain works, this will be industry defining. This is why Flexion is one of Europe's Most Ambitious Startups. 🇪🇺🔥 0:00 Intro 0:38 Inside a Lab with 14 Humanoids 2:17 What’s Actually Inside a Humanoid? 4:43 The Robot Navigates on Its Own 6:31 Why Grasping Is So Hard 7:01 Stairs, Pushes, and Balance Tests 7:55 Training 4,000 Robots in Parallel 10:50 Teleoperation vs Reinforcement Learning 14:35 The Case for Humanoids 15:55 Why Europe Could Win Robotics Please like & RT 🙏

Twitter boi, do you want to see a humanoid AI lab from the inside? Well I do ;) Join me to visit Flexion Robotics A world-leading lab building the AI brains for humanoids – in Zurich, one of the best hubs for Physical AI. Their goal? Build the Android of robotics. One operating system that works on any humanoid hardware. It is crazy what their robots can do. We pushed a robot on the stairs. It didn't care. They left one in a rainy forest it was never trained on. Chilled and hiked there. And because they let robots train on their own, from zero through reinforcement learning, the robots do odd inhuman things. Eg they stand up from the ground in ways no human ever would. If their humanoid brain works, this will be industry defining. This is why Flexion is one of Europe's Most Ambitious Startups. 🇪🇺🔥 0:00 Intro 0:38 Inside a Lab with 14 Humanoids 2:17 What’s Actually Inside a Humanoid? 4:43 The Robot Navigates on Its Own 6:31 Why Grasping Is So Hard 7:01 Stairs, Pushes, and Balance Tests 7:55 Training 4,000 Robots in Parallel 10:50 Teleoperation vs Reinforcement Learning 14:35 The Case for Humanoids 15:55 Why Europe Could Win Robotics Please like & RT 🙏

Andreas Klinger 🦾

14,491 次观看 • 3 个月前

China's Unitree's G1 robot is a great platform for humanoid robot researchers all over the world. Here, researchers at CMU pushes the boundaries of human-like motion. Also, look at the names of the researchers, what do you notice?

China's Unitree's G1 robot is a great platform for humanoid robot researchers all over the world. Here, researchers at CMU pushes the boundaries of human-like motion. Also, look at the names of the researchers, what do you notice?

Zhao DaShuai 东北进修🇨🇳 Commentary

130,007 次观看 • 1 年前

Meet Any2Track, a new two-stage RL framework designed to address a core challenge for humanoid robots: reliably tracking diverse, dynamic motions in the real world. The framework consists of two key components: AnyTracker, a general motion tracker, and AnyAdapter, a module that allows the robot to adapt to real-world disturbances like uneven terrain or external forces. This approach gives robots the stability needed for practical use. The system has been successfully deployed on a Unitree G1 humanoid robot with zero-shot sim2real transfer, performing exceptionally well in various motion tracking tasks under multiple real-world disturbances. Project: Codebase:

Meet Any2Track, a new two-stage RL framework designed to address a core challenge for humanoid robots: reliably tracking diverse, dynamic motions in the real world. The framework consists of two key components: AnyTracker, a general motion tracker, and AnyAdapter, a module that allows the robot to adapt to real-world disturbances like uneven terrain or external forces. This approach gives robots the stability needed for practical use. The system has been successfully deployed on a Unitree G1 humanoid robot with zero-shot sim2real transfer, performing exceptionally well in various motion tracking tasks under multiple real-world disturbances. Project: Codebase:

RoboHub🤖

61,378 次观看 • 9 个月前

Scientists developed HumanX, a system that lets humanoid robots learn new skills by watching just one short human video. It learns directly from demonstrations instead of large datasets or complex training rewards. Using this, the Unitree G1 humanoid robot learned 10 different real-world skills without extra training.

Scientists developed HumanX, a system that lets humanoid robots learn new skills by watching just one short human video. It learns directly from demonstrations instead of large datasets or complex training rewards. Using this, the Unitree G1 humanoid robot learned 10 different real-world skills without extra training.

Space and Technology

27,686 次观看 • 2 个月前

Humanoids finally move like humans… and can do more than copy. [Details + demos in thread 👇] A new framework, BeyondMimic, shows how to learn naturalistic whole-body control from human motion. But then goes further by composing those skills into versatile, zero-shot policies. Two key pieces make it work: •Motion tracking pipeline: robustly turns human mocap into dynamic robot motions (jump spins, sprinting, cartwheels) on real hardware •Guided diffusion policy: distills motion primitives and enables zero-shot downstream tasks with simple cost functions The result: ✅ State-of-the-art motion quality from mocap tracking ✅ Versatile task execution; waypoint navigation, joystick teleop, obstacle avoidance ✅ Stable sim-to-real transfer for diverse humanoid skills Thank you for sharing, Qiayuan Liao! 📍 Paper: Video:

Humanoids finally move like humans… and can do more than copy. [Details + demos in thread 👇] A new framework, BeyondMimic, shows how to learn naturalistic whole-body control from human motion. But then goes further by composing those skills into versatile, zero-shot policies. Two key pieces make it work: •Motion tracking pipeline: robustly turns human mocap into dynamic robot motions (jump spins, sprinting, cartwheels) on real hardware •Guided diffusion policy: distills motion primitives and enables zero-shot downstream tasks with simple cost functions The result: ✅ State-of-the-art motion quality from mocap tracking ✅ Versatile task execution; waypoint navigation, joystick teleop, obstacle avoidance ✅ Stable sim-to-real transfer for diverse humanoid skills Thank you for sharing, Qiayuan Liao! 📍 Paper: Video:

Ilir Aliu - eu/acc

72,522 次观看 • 10 个月前

Scientists have taught a humanoid robot to play tennis using just 5 hours of motion capture data. After training, the robot is capable of sustaining long rallies with a human and returning balls at speeds exceeding 15 m/s with an accuracy of about 90%.

Scientists have taught a humanoid robot to play tennis using just 5 hours of motion capture data. After training, the robot is capable of sustaining long rallies with a human and returning balls at speeds exceeding 15 m/s with an accuracy of about 90%.

Massimo

49,108 次观看 • 2 个月前

Open-source robot arm meets hand tracking [📍GitHub below] It is designed with an industrial mindset but built as a 3D-printed desktop system. PAROL6 paired with a LEAP Motion controller is a nice example of how accessible robot teleoperation has become. • Hand motion is streamed to the robot at 100 Hz via UDP • A pneumatic gripper is controlled by simple fist open and close gestures • The entire robot stack is open source, from mechanics to control software Combine that with low-latency hand tracking and you get a very practical platform for learning manipulation, teleoperation, and human-robot interfaces. This kind of setup is great for experimentation, teleop, data collection, and teaching robots by demonstration All without proprietary hardware or locked software. Credit to SourceRobotics 📍Code: —— Weekly robotics and AI insights. Subscribe free:

Open-source robot arm meets hand tracking [📍GitHub below] It is designed with an industrial mindset but built as a 3D-printed desktop system. PAROL6 paired with a LEAP Motion controller is a nice example of how accessible robot teleoperation has become. • Hand motion is streamed to the robot at 100 Hz via UDP • A pneumatic gripper is controlled by simple fist open and close gestures • The entire robot stack is open source, from mechanics to control software Combine that with low-latency hand tracking and you get a very practical platform for learning manipulation, teleoperation, and human-robot interfaces. This kind of setup is great for experimentation, teleop, data collection, and teaching robots by demonstration All without proprietary hardware or locked software. Credit to SourceRobotics 📍Code: —— Weekly robotics and AI insights. Subscribe free:

Ilir Aliu

32,025 次观看 • 5 个月前

A cute humanoid robot from NYC Fauna Robotics has released a humanoid robot called Sprout, which is approximately 1 meter tall, lightweight, quiet, and features no dangerous edges and soft skin. Its appearance and interactions are designed to be "approachable," like a social companion. It is currently offered as a general-purpose robotics platform for developers, providing basic movement, perception, navigation, and expression capabilities, allowing real-world use cases to emerge within the community. Many humanoid robots are born in factories and warehouses, but Fauna Robotics aims to bring humanoid robots back into human living spaces. Fauna believes that if humanoid robots are to become as ubiquitous as computers and mobile phones in the future, the starting point must be a safe, friendly form suitable for homes, schools, and offices. Sprout is their first step in this direction.

A cute humanoid robot from NYC Fauna Robotics has released a humanoid robot called Sprout, which is approximately 1 meter tall, lightweight, quiet, and features no dangerous edges and soft skin. Its appearance and interactions are designed to be "approachable," like a social companion. It is currently offered as a general-purpose robotics platform for developers, providing basic movement, perception, navigation, and expression capabilities, allowing real-world use cases to emerge within the community. Many humanoid robots are born in factories and warehouses, but Fauna Robotics aims to bring humanoid robots back into human living spaces. Fauna believes that if humanoid robots are to become as ubiquitous as computers and mobile phones in the future, the starting point must be a safe, friendly form suitable for homes, schools, and offices. Sprout is their first step in this direction.

CyberRobo

18,549 次观看 • 4 个月前

What if robots could learn real-world tasks from your perspective… without ever touching a robot? This is a system that trains robot policies using nothing but human-first, egocentric video data from smart glasses. No robots, no teleop, no sensors, just humans doing real tasks in the real world. Why it matters ✅ Learns robot policies from 20 minutes of human video; zero robot demos ✅ Generalizes to new objects, views, and even robot morphologies ✅ Uses 3D points for interpretable, spatially grounded learning ✅ Deploys directly to real-world robots with strong zero-shot success Thank you, Vincent Liu, for sharing!!! Learn more here: 🔗 Paper: 🌐 Website: 📍 BOOKMARK FOR LATER

What if robots could learn real-world tasks from your perspective… without ever touching a robot? This is a system that trains robot policies using nothing but human-first, egocentric video data from smart glasses. No robots, no teleop, no sensors, just humans doing real tasks in the real world. Why it matters ✅ Learns robot policies from 20 minutes of human video; zero robot demos ✅ Generalizes to new objects, views, and even robot morphologies ✅ Uses 3D points for interpretable, spatially grounded learning ✅ Deploys directly to real-world robots with strong zero-shot success Thank you, Vincent Liu, for sharing!!! Learn more here: 🔗 Paper: 🌐 Website: 📍 BOOKMARK FOR LATER

Ilir Aliu - eu/acc

10,509 次观看 • 1 年前