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Humanoid motion tracking performance is greatly determined by retargeting quality! Introducing 𝗢𝗺𝗻𝗶𝗥𝗲𝘁𝗮𝗿𝗴𝗲𝘁🎯, generating high-quality interaction-preserving data from human motions for learning complex humanoid skills with 𝗺𝗶𝗻𝗶𝗺𝗮𝗹 RL: - 5 rewards, - 4 DR terms, - Proprio. ONLY, - NO history/curriculum. Ready for agile, human-like 🤖? (Best with 🎧) 🔗... show more

Zhen Wu

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814,493 görüntüleme • 9 ay önce •via X (Twitter)

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Why do most humanoid motion trackers still slip, skate, or clip through objects? Because the real bottleneck isn’t RL, it’s bad retargeting… OmniRetarget flips the script: instead of compensating with dozens of reward terms, it generates high-quality, interaction-preserving trajectories from human motions. The result is agile, human-like skills with minimal RL. ✅ 5 reward terms, 4 domain randomization terms, proprioception only, no history or curriculum ✅ Interaction mesh produces clean, contact-preserving motions with virtually no foot-skating ✅ Works across embodiments (Unitree H1, Booster T1) and scales from one demo to many scenes From carrying boxes to slope crawling to parkour-style rolls, policies trained on OmniRetarget data transfer zero-shot from sim to real hardware. Thanks for sharing, Zhen Wu! 📍Paper: Website: Dataset:

Why do most humanoid motion trackers still slip, skate, or clip through objects? Because the real bottleneck isn’t RL, it’s bad retargeting… OmniRetarget flips the script: instead of compensating with dozens of reward terms, it generates high-quality, interaction-preserving trajectories from human motions. The result is agile, human-like skills with minimal RL. ✅ 5 reward terms, 4 domain randomization terms, proprioception only, no history or curriculum ✅ Interaction mesh produces clean, contact-preserving motions with virtually no foot-skating ✅ Works across embodiments (Unitree H1, Booster T1) and scales from one demo to many scenes From carrying boxes to slope crawling to parkour-style rolls, policies trained on OmniRetarget data transfer zero-shot from sim to real hardware. Thanks for sharing, Zhen Wu! 📍Paper: Website: Dataset:

Ilir Aliu - eu/acc

61,491 görüntüleme • 9 ay önce

🤖 Introducing H2O (Human2HumanOid): - 🧠 An RL-based human-to-humanoid real-time whole-body teleoperation framework - 💃 Scalable retargeting and training using large human motion dataset - 🎥 With just an RGB camera, everyone can teleoperate a full-sized humanoid to perform actions like pick and place, walking, kicking, boxing, etc - 💡Unleash the potential of humanoids with human cognitive skills and adaptability 🔗:

🤖 Introducing H2O (Human2HumanOid): - 🧠 An RL-based human-to-humanoid real-time whole-body teleoperation framework - 💃 Scalable retargeting and training using large human motion dataset - 🎥 With just an RGB camera, everyone can teleoperate a full-sized humanoid to perform actions like pick and place, walking, kicking, boxing, etc - 💡Unleash the potential of humanoids with human cognitive skills and adaptability 🔗:

Tairan He

96,738 görüntüleme • 2 yıl önce

🎾Your humanoid tennis player is here!🤖 Introducing LATENT (Learning Athletic Humanoid Tennis Skills from Imperfect Human Motion Data) — the world’s first real-time whole-body planning and control algorithm for athletic humanoid tennis. For the first time, a humanoid robot can sustain high-dynamic, long-horizon tennis rallies with millisecond-level reactions, precise ball striking, and natural whole-body motion. This marks a leap from mechanical motion imitation to intelligent, decision-driven athletic interaction. #Galbot #Robotics #Innovation #AI #Technology #Humanoid

🎾Your humanoid tennis player is here!🤖 Introducing LATENT (Learning Athletic Humanoid Tennis Skills from Imperfect Human Motion Data) — the world’s first real-time whole-body planning and control algorithm for athletic humanoid tennis. For the first time, a humanoid robot can sustain high-dynamic, long-horizon tennis rallies with millisecond-level reactions, precise ball striking, and natural whole-body motion. This marks a leap from mechanical motion imitation to intelligent, decision-driven athletic interaction. #Galbot #Robotics #Innovation #AI #Technology #Humanoid

Galbot

45,731 görüntüleme • 4 ay önce

I've long wondered if we can make a humanoid robot do a 𝘄𝗮𝗹𝗹𝗳𝗹𝗶𝗽 - and we just made it happen by leveraging 𝗢𝗺𝗻𝗶𝗥𝗲𝘁𝗮𝗿𝗴𝗲𝘁 with BeyondMimic tracking! This came after our original OmniRetarget experiments, with only minor tweaks to RL training: relaxing a termination threshold and removing one reward term. The policy achieved a 𝟱/𝟱 success rate in our real-world experiments, showing the strength of high-quality, interaction-preserving motion retargeting combined with BeyondMimic’s minimal RL tracking. Here is the updated arXiv: (In Sec. V. A)

I've long wondered if we can make a humanoid robot do a 𝘄𝗮𝗹𝗹𝗳𝗹𝗶𝗽 - and we just made it happen by leveraging 𝗢𝗺𝗻𝗶𝗥𝗲𝘁𝗮𝗿𝗴𝗲𝘁 with BeyondMimic tracking! This came after our original OmniRetarget experiments, with only minor tweaks to RL training: relaxing a termination threshold and removing one reward term. The policy achieved a 𝟱/𝟱 success rate in our real-world experiments, showing the strength of high-quality, interaction-preserving motion retargeting combined with BeyondMimic’s minimal RL tracking. Here is the updated arXiv: (In Sec. V. A)

Zhen Wu

1,051,049 görüntüleme • 9 ay önce

HDMI (HumanoiD iMitation for Interaction) is a framework enabling humanoid robots to learn whole-body object interaction skills from monocular RGB human videos. It extracts and retargets human poses and object trajectories using GVHMR and LocoMujoco, building reference datasets with contact annotations. The data is used to train an RL policy via robot-object co-tracking. HDMI achieved 67 consecutive door traversals.

HDMI (HumanoiD iMitation for Interaction) is a framework enabling humanoid robots to learn whole-body object interaction skills from monocular RGB human videos. It extracts and retargets human poses and object trajectories using GVHMR and LocoMujoco, building reference datasets with contact annotations. The data is used to train an RL policy via robot-object co-tracking. HDMI achieved 67 consecutive door traversals.

The Humanoid Hub

17,395 görüntüleme • 9 ay önce

🤔 Ever wondered if simulation-based animation/avatar learnings can be applied to real humanoid in real-time? 🤖 Introducing H2O (Human2HumanOid): - 🧠 An RL-based human-to-humanoid real-time whole-body teleoperation framework - 💃 Scalable retargeting and training using large human motion dataset - 🎥 With just an RGB camera, everyone can teleoperate a full-sized humanoid to perform actions like pick and place, walking, kicking, boxing, etc - 💡Unleash the potential of humanoids with human cognitive skills and adaptability 🔗: 📄: 🎬: H2O proposes: - A scalable retargeting framework for obtaining large-scale humanoid motion dataset, intelligently filtering out infeasible motion for the humanoid embodiment. - We train a full-body motion imitator (similar to PHC) and deploy to the real world zero-shot. - Using this framework, we enable real-time teleoperation of a humanoid via a human operator and webcam, performing skills such as pick and place, kicking, walking strollers, etc. Team: Tairan He Zhengyi “Zen” Luo Wenli Xiao @ChongZitaZhang Kris Kitani Changliu Liu Guanya Shi

Zhengyi “Zen” Luo

47,305 görüntüleme • 2 yıl önce

There are many ways to collect high-quality data. GenRobot seems to be closer to human-like methods. It uses a wearable exoskeleton robotic hand: a person simply wears the Gen DAS Dex and can flexibly complete various tasks, while simultaneously capturing the entire human motion trajectory and converting it into high-quality data for use by the humanoid robot. >23 degrees of freedom omnidirectional hand capture with millimeter-level precision >Tactile and visual fusion >Sub-millisecond head-hand synchronization

There are many ways to collect high-quality data. GenRobot seems to be closer to human-like methods. It uses a wearable exoskeleton robotic hand: a person simply wears the Gen DAS Dex and can flexibly complete various tasks, while simultaneously capturing the entire human motion trajectory and converting it into high-quality data for use by the humanoid robot. >23 degrees of freedom omnidirectional hand capture with millimeter-level precision >Tactile and visual fusion >Sub-millisecond head-hand synchronization

CyberRobo

18,098 görüntüleme • 3 ay önce

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 görüntüleme • 11 ay önce

The power of generative models — now embodied in humanoids. Announcing DreamControl –– After a year-long research effort at General Robotics — we present a scalable framework for whole-body humanoid control that fuses diffusion priors with reinforcement learning to unlock real-world scene interaction. Diffusion + RL → natural whole-body skills on real robots. DreamControl enables humanoids to move beyond locomotion demos → performing natural, human-like skills such as –– Picking & lifting objects, Opening drawers & doors, Precise punching, kicking, and jumping, Bimanual manipulation tasks Our key innovation: a diffusion prior over human motion that guides RL, eliminating the need for massive teleoperation datasets, and producing motions that look human while transferring to real hardware. Trained purely in simulation, deployed on the Unitree G1 humanoid, DreamControl policies run in real time, bridging sim-to-real with unprecedented naturalness. We leverage a novel hybrid edge + cloud infrastructure that runs RL-trained policies on the edge backed by powerful AI models running in the cloud This is the next step in General Robotics’ journey toward general-purpose humanoid assistants that interact, adapt, and assist autonomously. Paper: Blog: 1/n

The power of generative models — now embodied in humanoids. Announcing DreamControl –– After a year-long research effort at General Robotics — we present a scalable framework for whole-body humanoid control that fuses diffusion priors with reinforcement learning to unlock real-world scene interaction. Diffusion + RL → natural whole-body skills on real robots. DreamControl enables humanoids to move beyond locomotion demos → performing natural, human-like skills such as –– Picking & lifting objects, Opening drawers & doors, Precise punching, kicking, and jumping, Bimanual manipulation tasks Our key innovation: a diffusion prior over human motion that guides RL, eliminating the need for massive teleoperation datasets, and producing motions that look human while transferring to real hardware. Trained purely in simulation, deployed on the Unitree G1 humanoid, DreamControl policies run in real time, bridging sim-to-real with unprecedented naturalness. We leverage a novel hybrid edge + cloud infrastructure that runs RL-trained policies on the edge backed by powerful AI models running in the cloud This is the next step in General Robotics’ journey toward general-purpose humanoid assistants that interact, adapt, and assist autonomously. Paper: Blog: 1/n

Ashish Kapoor

118,133 görüntüleme • 9 ay önce

How do you give a humanoid the general motion capability? Not just single motions, but all motion? Introducing SONIC, our new work on supersizing motion tracking for natural humanoid control. We argue that motion tracking is the scalable foundation task for humanoids. So we "supersized" it: 9k+ GPU hours and 100M+ motion frames. But tracking alone is not enough; we show how to make a useful control system out of it: - Universal Kinematic Planner: Enables game-like gamepad control and high-level teleoperation, just like controlling a character in a game. - VR Full-Body Teleop: Direct, real-time whole-body control by a human wearing a VR headset. - VR Keypoint Teleop: Control the upper body (hands/head) while our planner handles robust locomotion automatically. - VLA Integration: We connect this motion tracker to autonomous Visual-Language-Action (VLA) models for autonomous task execution! We use a Universal Token Space to UNIFY this command space, turning our robust tracker into a general-purpose, programmable humanoid brain. This is the generalist "System 1" for humanoids. 🚀 Project: #Humanoids #Robotics #AI #FoundationModels #NVIDIAResearch 🧠🔥

How do you give a humanoid the general motion capability? Not just single motions, but all motion? Introducing SONIC, our new work on supersizing motion tracking for natural humanoid control. We argue that motion tracking is the scalable foundation task for humanoids. So we "supersized" it: 9k+ GPU hours and 100M+ motion frames. But tracking alone is not enough; we show how to make a useful control system out of it: - Universal Kinematic Planner: Enables game-like gamepad control and high-level teleoperation, just like controlling a character in a game. - VR Full-Body Teleop: Direct, real-time whole-body control by a human wearing a VR headset. - VR Keypoint Teleop: Control the upper body (hands/head) while our planner handles robust locomotion automatically. - VLA Integration: We connect this motion tracker to autonomous Visual-Language-Action (VLA) models for autonomous task execution! We use a Universal Token Space to UNIFY this command space, turning our robust tracker into a general-purpose, programmable humanoid brain. This is the generalist "System 1" for humanoids. 🚀 Project: #Humanoids #Robotics #AI #FoundationModels #NVIDIAResearch 🧠🔥

Zhengyi “Zen” Luo

62,040 görüntüleme • 8 ay önce

Sports like tennis are great examples of the sort of dynamic whole-body interaction that’s possible with humanoid robots. But capturing examples of fast, dynamic interactions from humans is really difficult. Enter LATENT, which uses lower-quality human data plus reinforcement learning to teach a robot to play tennis, able to complete back-and-forth volleys at a human level. LATENT has three steps: (1) collecting imperfect human data like a backswing, (2) using these to learn a latent action space, and (3) they train a high-level policy in simulation which can compose these actions and execute tennis skills on a robot. Haofei Lu and Yunrui Lian join us to tell us about their method. Watch Episode #80 of RoboPapers, with Chris Paxton and Jiafei Duan, now to learn more!

Sports like tennis are great examples of the sort of dynamic whole-body interaction that’s possible with humanoid robots. But capturing examples of fast, dynamic interactions from humans is really difficult. Enter LATENT, which uses lower-quality human data plus reinforcement learning to teach a robot to play tennis, able to complete back-and-forth volleys at a human level. LATENT has three steps: (1) collecting imperfect human data like a backswing, (2) using these to learn a latent action space, and (3) they train a high-level policy in simulation which can compose these actions and execute tennis skills on a robot. Haofei Lu and Yunrui Lian join us to tell us about their method. Watch Episode #80 of RoboPapers, with Chris Paxton and Jiafei Duan, now to learn more!

RoboPapers

29,886 görüntüleme • 2 ay önce

🚀 Struggling with the lack of high-quality data for AI-driven human-object interaction research? We've got you covered! Introducing HUMOTO, a groundbreaking 4D dataset for human-object interaction, developed with a combination of wearable motion capture, SOTA 6D pose estimation vision models, LLM, and the professional refining works of multiple animation studios. HUMOTO features: ✅ Over 700 diverse daily activities ✅ Interactions with 60+ objects, 70+ articulated parts. ✅ Fine-grained text annotations ✅ Detailed hand and finger movements We hope HUMOTO will fuel your Humanoid AI research and drive new advancements! For research or commercial license inquiries, please contact yizho@adobe.com. Explore the dataset: 👉 HUMOTO Dataset Website Learn more: 👉 HUMOTO Project Page Jiaxin Lu Qixing Huang #AI #HumanObjectInteraction #HumanoidAI #MotionCapture #HUMOTO #AdobeResearch

🚀 Struggling with the lack of high-quality data for AI-driven human-object interaction research? We've got you covered! Introducing HUMOTO, a groundbreaking 4D dataset for human-object interaction, developed with a combination of wearable motion capture, SOTA 6D pose estimation vision models, LLM, and the professional refining works of multiple animation studios. HUMOTO features: ✅ Over 700 diverse daily activities ✅ Interactions with 60+ objects, 70+ articulated parts. ✅ Fine-grained text annotations ✅ Detailed hand and finger movements We hope HUMOTO will fuel your Humanoid AI research and drive new advancements! For research or commercial license inquiries, please contact [email protected]. Explore the dataset: 👉 HUMOTO Dataset Website Learn more: 👉 HUMOTO Project Page Jiaxin Lu Qixing Huang #AI #HumanObjectInteraction #HumanoidAI #MotionCapture #HUMOTO #AdobeResearch

Yi Zhou

71,562 görüntüleme • 1 yıl önce

NEWS: Humanoid robotics company Figure has released Helix 02, what they claim in their most capable humanoid model yet. "A single neural system that controls the full body directly from pixels, enabling dexterous, long horizon autonomy across an entire room: • Autonomous, long‑horizon loco-manipulation: Helix 02 unloads and reloads a dishwasher across a full-sized kitchen - a four-minute, end-to-end autonomous task that integrates walking, manipulation, and balance with no resets and no human intervention. We believe this is the longest horizon, most complex task completed autonomously by a humanoid robot to date. • All sensors in. All actuators out: Helix 02 connects every onboard sensor - vision, touch, and proprioception - directly to every actuator through a single unified visuomotor neural network. • Human-like whole body control from human data: All results are enabled by System 0, a learned whole‑body controller trained on over 1,000 hours of human motion data and sim‑to‑real reinforcement learning. System 0 replaces 109,504 lines of hand‑engineered C++ with a single neural prior for stable, natural motion. • New classes of dexterity: With Figure 03’s embedded tactile sensing and palm cameras, Helix 02 performs manipulation that was previously out of reach: extracting individual pills, dispensing precise syringe volumes, and singulating small, irregular objects from clutter despite self‑occlusion. Helix 02 is trained on over 1,000 hours of human motion data and integrates vision, touch, and proprioception."

NEWS: Humanoid robotics company Figure has released Helix 02, what they claim in their most capable humanoid model yet. "A single neural system that controls the full body directly from pixels, enabling dexterous, long horizon autonomy across an entire room: • Autonomous, long‑horizon loco-manipulation: Helix 02 unloads and reloads a dishwasher across a full-sized kitchen - a four-minute, end-to-end autonomous task that integrates walking, manipulation, and balance with no resets and no human intervention. We believe this is the longest horizon, most complex task completed autonomously by a humanoid robot to date. • All sensors in. All actuators out: Helix 02 connects every onboard sensor - vision, touch, and proprioception - directly to every actuator through a single unified visuomotor neural network. • Human-like whole body control from human data: All results are enabled by System 0, a learned whole‑body controller trained on over 1,000 hours of human motion data and sim‑to‑real reinforcement learning. System 0 replaces 109,504 lines of hand‑engineered C++ with a single neural prior for stable, natural motion. • New classes of dexterity: With Figure 03’s embedded tactile sensing and palm cameras, Helix 02 performs manipulation that was previously out of reach: extracting individual pills, dispensing precise syringe volumes, and singulating small, irregular objects from clutter despite self‑occlusion. Helix 02 is trained on over 1,000 hours of human motion data and integrates vision, touch, and proprioception."

Sawyer Merritt

624,689 görüntüleme • 5 ay önce

ROBOTIS just upgraded its wheeled humanoid with the HX5-D20 dexterous hand 👋 •»20 DOF fully active,5 fingers × 4 DOF each. Human-like motion, from piano keys to scissors. • »Direct drive, wire-free ,Powered by ultra-small Dynamixel actuators. Zero backlash. High durability. Clean control. • »Fingertip tactile sensing ,Real-time pressure feedback for force-controlled grasping, blind operation, and fragile object handling. Already deployed at Google, Apple, and MIT labs.

ROBOTIS just upgraded its wheeled humanoid with the HX5-D20 dexterous hand 👋 •»20 DOF fully active,5 fingers × 4 DOF each. Human-like motion, from piano keys to scissors. • »Direct drive, wire-free ,Powered by ultra-small Dynamixel actuators. Zero backlash. High durability. Clean control. • »Fingertip tactile sensing ,Real-time pressure feedback for force-controlled grasping, blind operation, and fragile object handling. Already deployed at Google, Apple, and MIT labs.

CyberRobo

42,010 görüntüleme • 5 ay önce

💡Divergence thinking💡 is a hallmark of human creativity and problem-solving 🤖Can LLMs also do divergent reasoning to generate diverse solutions🤔? Introducing Flow-of-Reasoning (FoR) 🌊, a data-efficient way of training LLM policy to generate diverse, high-quality reasoning trajectories Unlike existing RL (like PPO) and planning (like MCTS) to find the max-reward trajectory (akin to convergent thinking), FoR connects LLM reasoning with the #GFlowNet formulation and enables LLMs to find trajectories proportional to reward distribution. 🎬The demo video illustrates how FoR learns and infers multiple solutions to a ♠️Game24 puzzle. 🎯Inferring for diverse solutions could be useful for robustness, data augmentation, and enhanced model generalization. Project page: Paper: Github:

💡Divergence thinking💡 is a hallmark of human creativity and problem-solving 🤖Can LLMs also do divergent reasoning to generate diverse solutions🤔? Introducing Flow-of-Reasoning (FoR) 🌊, a data-efficient way of training LLM policy to generate diverse, high-quality reasoning trajectories Unlike existing RL (like PPO) and planning (like MCTS) to find the max-reward trajectory (akin to convergent thinking), FoR connects LLM reasoning with the #GFlowNet formulation and enables LLMs to find trajectories proportional to reward distribution. 🎬The demo video illustrates how FoR learns and infers multiple solutions to a ♠️Game24 puzzle. 🎯Inferring for diverse solutions could be useful for robustness, data augmentation, and enhanced model generalization. Project page: Paper: Github:

Lianhui Qin

50,447 görüntüleme • 2 yıl önce

X-Humanoid just officially dropped Embodied Tien Kung 3.0, A universal platform designed to be way more open and developer-friendly. 🤖 Built on their Wise Kaiwu AI platform, this next-gen humanoid is all about slashing development costs. It’s a fully interoperable ecosystem that supports everything from tactile interaction to high-dynamic motion control at a full humanoid scale. ➤ Radical Openness: X-Humanoid is open-sourcing the full stack—robot body, motion control, VLM/VLA models, and the RoboMIND dataset. It fully supports ROS2, MQTT, and TCP/IP, so developers can customize use cases without re-engineering the basics. ➤ High-Performance Hardware: With high-torque integrated joints, Tien Kung 3.0 can clear 1-meter (3.3ft) obstacles and handle dexterous moves like kneeling and bending. It hits millimeter-level precision, making it a solid fit for industrial-grade tasks. ➤ True Autonomy: The bot runs a continuous perception-decision-execution loop. It uses world models to break down complex language commands and VLA models for real-time obstacle avoidance and navigation. ➤ Scalable Collaboration: The platform moves beyond single-unit tasks to support multi-robot collaboration with autonomous scheduling. It’s built to move embodied AI from the lab straight into real-world commercial and industrial environments. Source: X-Humanoid #Humanoid #OpenSource #Robotics #EmbodiedAI #PhysicalAI #Automation #XHumanoid #TienKung #WiseKaiwu

X-Humanoid just officially dropped Embodied Tien Kung 3.0, A universal platform designed to be way more open and developer-friendly. 🤖 Built on their Wise Kaiwu AI platform, this next-gen humanoid is all about slashing development costs. It’s a fully interoperable ecosystem that supports everything from tactile interaction to high-dynamic motion control at a full humanoid scale. ➤ Radical Openness: X-Humanoid is open-sourcing the full stack—robot body, motion control, VLM/VLA models, and the RoboMIND dataset. It fully supports ROS2, MQTT, and TCP/IP, so developers can customize use cases without re-engineering the basics. ➤ High-Performance Hardware: With high-torque integrated joints, Tien Kung 3.0 can clear 1-meter (3.3ft) obstacles and handle dexterous moves like kneeling and bending. It hits millimeter-level precision, making it a solid fit for industrial-grade tasks. ➤ True Autonomy: The bot runs a continuous perception-decision-execution loop. It uses world models to break down complex language commands and VLA models for real-time obstacle avoidance and navigation. ➤ Scalable Collaboration: The platform moves beyond single-unit tasks to support multi-robot collaboration with autonomous scheduling. It’s built to move embodied AI from the lab straight into real-world commercial and industrial environments. Source: X-Humanoid #Humanoid #OpenSource #Robotics #EmbodiedAI #PhysicalAI #Automation #XHumanoid #TienKung #WiseKaiwu

RoboHub🤖

49,440 görüntüleme • 5 ay önce

1X announces their latest reinforcement learning (RL) controller, which unlocks NEO's full-body mobility for home environments, enabling Redwood AI (1X's in-house AI model) to interact with the physical world more naturally and broadly. The unified controller supports walking in any direction, sitting, standing, kneeling, lying down, getting up, and climbing stairs using stereo RGB vision - critical for navigating real homes. The controller provides an “action interface” through which teleoperation or Redwood AI can interact in a safe, contact-rich manner with the physical world. Traditional walking controllers often rely on hand-crafted "shaping rewards" to produce human-like gaits, which are time-consuming and don’t scale well across tasks or movement directions. To address this, the team used motion capture data to guide the learning process. The RL system is trained to track kinematic reference trajectories derived from natural human motion while maintaining balance and rhythm in real time. To improve general-purpose utility, the controller goes beyond single-trajectory replay by introducing a two-part design: ⦿ A high-level kinematic planner generates smooth, human-like movement goals from simple input commands (e.g., joystick direction). ⦿ A low-level RL controller tracks these trajectories with dynamic stability. This structure enables smooth transitions between behaviors and resolves the challenge of controlling high-dimensional motion from coarse user input.

1X announces their latest reinforcement learning (RL) controller, which unlocks NEO's full-body mobility for home environments, enabling Redwood AI (1X's in-house AI model) to interact with the physical world more naturally and broadly. The unified controller supports walking in any direction, sitting, standing, kneeling, lying down, getting up, and climbing stairs using stereo RGB vision - critical for navigating real homes. The controller provides an “action interface” through which teleoperation or Redwood AI can interact in a safe, contact-rich manner with the physical world. Traditional walking controllers often rely on hand-crafted "shaping rewards" to produce human-like gaits, which are time-consuming and don’t scale well across tasks or movement directions. To address this, the team used motion capture data to guide the learning process. The RL system is trained to track kinematic reference trajectories derived from natural human motion while maintaining balance and rhythm in real time. To improve general-purpose utility, the controller goes beyond single-trajectory replay by introducing a two-part design: ⦿ A high-level kinematic planner generates smooth, human-like movement goals from simple input commands (e.g., joystick direction). ⦿ A low-level RL controller tracks these trajectories with dynamic stability. This structure enables smooth transitions between behaviors and resolves the challenge of controlling high-dimensional motion from coarse user input.

The Humanoid Hub

113,780 görüntüleme • 1 yıl önce

I don’t know if we live in a Matrix, but I know for sure that robots will spend most of their lives in simulation. Let machines train machines. I’m excited to introduce DexMimicGen, a massive-scale synthetic data generator that enables a humanoid robot to learn complex skills from only a handful of human demonstrations. Yes, as few as 5! DexMimicGen addresses the biggest pain point in robotics: where do we get data? Unlike with LLMs, where vast amounts of texts are readily available, you cannot simply download motor control signals from the internet. So researchers teleoperate the robots to collect motion data via XR headsets. They have to repeat the same skill over and over and over again, because neural nets are data hungry. This is a very slow and uncomfortable process. At NVIDIA, we believe the majority of high-quality tokens for robot foundation models will come from simulation. What DexMimicGen does is to trade GPU compute time for human time. It takes one motion trajectory from human, and multiplies into 1000s of new trajectories. A robot brain trained on this augmented dataset will generalize far better in the real world. Think of DexMimicGen as a learning signal amplifier. It maps a small dataset to a large (de facto infinite) dataset, using physics simulation in the loop. In this way, we free humans from babysitting the bots all day. The future of robot data is generative. The future of the entire robot learning pipeline will also be generative. 🧵

I don’t know if we live in a Matrix, but I know for sure that robots will spend most of their lives in simulation. Let machines train machines. I’m excited to introduce DexMimicGen, a massive-scale synthetic data generator that enables a humanoid robot to learn complex skills from only a handful of human demonstrations. Yes, as few as 5! DexMimicGen addresses the biggest pain point in robotics: where do we get data? Unlike with LLMs, where vast amounts of texts are readily available, you cannot simply download motor control signals from the internet. So researchers teleoperate the robots to collect motion data via XR headsets. They have to repeat the same skill over and over and over again, because neural nets are data hungry. This is a very slow and uncomfortable process. At NVIDIA, we believe the majority of high-quality tokens for robot foundation models will come from simulation. What DexMimicGen does is to trade GPU compute time for human time. It takes one motion trajectory from human, and multiplies into 1000s of new trajectories. A robot brain trained on this augmented dataset will generalize far better in the real world. Think of DexMimicGen as a learning signal amplifier. It maps a small dataset to a large (de facto infinite) dataset, using physics simulation in the loop. In this way, we free humans from babysitting the bots all day. The future of robot data is generative. The future of the entire robot learning pipeline will also be generative. 🧵

Jim Fan

165,246 görüntüleme • 1 yıl önce

China unveils humanoid robot with lifelike skin and blinking eyes built for daily life | Prabhat Ranjan Mishra, Interesting Engineering Large Language Models (LLMs) and Vision-Language Models (VLMs) help process and interpret complex data from human interactions. A Shanghai-based company has developed humanoid robots that appear as real as humans. The advanced bionic humanoid robot is integrated with self-supervised AI algorithms. Named Elf V1, the robot can perceive the world, communicate, learn, and interact intelligently with its surroundings. Developed by AheadForm Technology, the robot offers up to 30 degrees of freedom, powered by a precise control system and an advanced AI learning algorithm. Robot offers expressive facial features The robot offers expressive facial features, moving eyes, and synchronized speech. It can also convey emotions and understand human non-verbal cues, making interactions more natural and engaging. The robot has highly interactive capabilities and lifelike appearances. AheadForm expects that its robots could soon seamlessly integrate into daily life, providing assistance, companionship, and support across various industries. “We believe that by developing realistic and expressive robot heads, we can bridge the gap between humans and machines, fostering a new era of interactive and intelligent robotics,” said the company in a statement. Reports revealed that to avoid the “uncanny valley” effect and be able to interact with us, they are given lifelike skin and capabilities to read our emotions and respond appropriately using dynamic expression simulation and emotion generation tech. Bionic skin and high-precision control system The Elf V1 series of humanoids features 30 facial muscles animated by brushless micro-motors and managed by a high-precision control system. Paired with an ability to detect their users’ emotions with low latency and bionic skin, their facial expressions are nearly identical to those of humans, reported CGTN. The company claims it’s pioneering the development of realistic humanoid robots designed to revolutionize human-robot interaction. It’s enhancing sophisticated humanoid robot heads that can express emotions, perceive their environment, and interact seamlessly with humans. By combining cutting-edge AI and advanced robotics, AheadForm aims to bring life to machines and transform how humans engage with technology. AI models boost robots’ responsiveness Seamless integration of Large Language Models (LLMs) and Vision-Language Models (VLMs) into the humanoid robots can help them process and interpret complex data from human interactions, enabling the robot to learn and adapt in real-time, achieving human-level understanding and responsiveness. AheadForm uses Brushless Motors that deliver ultra-quiet operation and high responsiveness, specifically designed for precision facial movements in humanoid robots. With its compact size, lightweight design, and energy efficiency, this motor is the ideal choice for next-generation robots that require precise, subtle facial control to create a truly human-like experience. Previously, the company unveiled the Lan Series that features realistic humanoid robots with soft skin and 10 degrees of freedom, offering a lifelike appearance and intuitive movements. This series is designed for cost-efficiency, for applications prioritizing mobility and manipulation.

China unveils humanoid robot with lifelike skin and blinking eyes built for daily life | Prabhat Ranjan Mishra, Interesting Engineering Large Language Models (LLMs) and Vision-Language Models (VLMs) help process and interpret complex data from human interactions. A Shanghai-based company has developed humanoid robots that appear as real as humans. The advanced bionic humanoid robot is integrated with self-supervised AI algorithms. Named Elf V1, the robot can perceive the world, communicate, learn, and interact intelligently with its surroundings. Developed by AheadForm Technology, the robot offers up to 30 degrees of freedom, powered by a precise control system and an advanced AI learning algorithm. Robot offers expressive facial features The robot offers expressive facial features, moving eyes, and synchronized speech. It can also convey emotions and understand human non-verbal cues, making interactions more natural and engaging. The robot has highly interactive capabilities and lifelike appearances. AheadForm expects that its robots could soon seamlessly integrate into daily life, providing assistance, companionship, and support across various industries. “We believe that by developing realistic and expressive robot heads, we can bridge the gap between humans and machines, fostering a new era of interactive and intelligent robotics,” said the company in a statement. Reports revealed that to avoid the “uncanny valley” effect and be able to interact with us, they are given lifelike skin and capabilities to read our emotions and respond appropriately using dynamic expression simulation and emotion generation tech. Bionic skin and high-precision control system The Elf V1 series of humanoids features 30 facial muscles animated by brushless micro-motors and managed by a high-precision control system. Paired with an ability to detect their users’ emotions with low latency and bionic skin, their facial expressions are nearly identical to those of humans, reported CGTN. The company claims it’s pioneering the development of realistic humanoid robots designed to revolutionize human-robot interaction. It’s enhancing sophisticated humanoid robot heads that can express emotions, perceive their environment, and interact seamlessly with humans. By combining cutting-edge AI and advanced robotics, AheadForm aims to bring life to machines and transform how humans engage with technology. AI models boost robots’ responsiveness Seamless integration of Large Language Models (LLMs) and Vision-Language Models (VLMs) into the humanoid robots can help them process and interpret complex data from human interactions, enabling the robot to learn and adapt in real-time, achieving human-level understanding and responsiveness. AheadForm uses Brushless Motors that deliver ultra-quiet operation and high responsiveness, specifically designed for precision facial movements in humanoid robots. With its compact size, lightweight design, and energy efficiency, this motor is the ideal choice for next-generation robots that require precise, subtle facial control to create a truly human-like experience. Previously, the company unveiled the Lan Series that features realistic humanoid robots with soft skin and 10 degrees of freedom, offering a lifelike appearance and intuitive movements. This series is designed for cost-efficiency, for applications prioritizing mobility and manipulation.

Owen Gregorian

179,005 görüntüleme • 9 ay önce

Physics-based Motion Retargeting from Sparse Inputs paper page: Avatars are important to create interactive and immersive experiences in virtual worlds. One challenge in animating these characters to mimic a user's motion is that commercial AR/VR products consist only of a headset and controllers, providing very limited sensor data of the user's pose. Another challenge is that an avatar might have a different skeleton structure than a human and the mapping between them is unclear. In this work we address both of these challenges. We introduce a method to retarget motions in real-time from sparse human sensor data to characters of various morphologies. Our method uses reinforcement learning to train a policy to control characters in a physics simulator. We only require human motion capture data for training, without relying on artist-generated animations for each avatar. This allows us to use large motion capture datasets to train general policies that can track unseen users from real and sparse data in real-time. We demonstrate the feasibility of our approach on three characters with different skeleton structure: a dinosaur, a mouse-like creature and a human. We show that the avatar poses often match the user surprisingly well, despite having no sensor information of the lower body available. We discuss and ablate the important components in our framework, specifically the kinematic retargeting step, the imitation, contact and action reward as well as our asymmetric actor-critic observations. We further explore the robustness of our method in a variety of settings including unbalancing, dancing and sports motions.

AK

106,527 görüntüleme • 3 yıl önce