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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...

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The Humanoid Hub

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17,395 просмотров • 9 месяцев назад •via X (Twitter)

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HumanX: a scalable framework that converts single monocular human videos into agile, generalizable interaction skills for humanoid robots without task-specific rewards. Core parts: - XGen: retargets human motion + synthesizes diverse physically plausible training data (via physics-driven object trajectories and augmentation). - XMimic: unified imitation pipeline for robust generalization. Shows promising results for both Blind (no external sensors) and MoCap modes.
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HumanX: a scalable framework that converts single monocular human videos into agile, generalizable interaction skills for humanoid robots without task-specific rewards. Core parts: - XGen: retargets human motion + synthesizes diverse physically plausible training data (via physics-driven object trajectories and augmentation). - XMimic: unified imitation pipeline for robust generalization. Shows promising results for both Blind (no external sensors) and MoCap modes.

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HOI-DETR (Hand-Object Interaction) is a new zero-shot framework that integrates hand-object and object-object interaction -Hand + tool detections -Interaction links (+secondary object) Huge for robotics training 🔥🔥
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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.
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RL-trained in-hand skills, such as grasp stability and object rotation are used to assist teleoperation. Teleoperation data is then used to train a VLA that fuses force and tactile feedback to generate actions. Humanoid hardware: Sharpa Paper:
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RL-trained in-hand skills, such as grasp stability and object rotation are used to assist teleoperation. Teleoperation data is then used to train a VLA that fuses force and tactile feedback to generate actions. Humanoid hardware: Sharpa Paper:

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Proof that human videos can be converted into context-aware humanoid locomotion. VideoMimic is a real-to-sim-to-real pipeline that reconstructs humans and scenes from video, retargets motion to a humanoid, and trains a single policy in sim immediately deployable on real robots.
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Proof that human videos can be converted into context-aware humanoid locomotion. VideoMimic is a real-to-sim-to-real pipeline that reconstructs humans and scenes from video, retargets motion to a humanoid, and trains a single policy in sim immediately deployable on real robots.

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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 🎧) 🔗 🎥 1/9
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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 🎧) 🔗 🎥 1/9

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Robots has a data problem, in that robotics data is rare. While human video is quite common, it’s not usually directly usable for robots for a variety of reasons, most significantly that it’s missing explicit, accurate robot actions. Instead, Jeremy Collins proposes that we predict keypoint trajectories — basically, how any given point in an object will move as a robot performs a task. This lets us use action-free human video to train robot skills. Learn more by watching Episode #37 of RoboPapers with Michael Cho - Rbt/Acc and Chris Paxton now.

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The most frustrating part of imitation learning is collecting huge amounts of teleop data. But why teleop robots when robots can learn by watching us? Introducing Point Policy, a novel framework that enables robots to learn from human videos without any teleop, sim2real, or RL.
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The most frustrating part of imitation learning is collecting huge amounts of teleop data. But why teleop robots when robots can learn by watching us? Introducing Point Policy, a novel framework that enables robots to learn from human videos without any teleop, sim2real, or RL.

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We can convert human videos to robot hand-object interaction trajectories in 4D. Enjoy! Paper: Website: Code: Authors:Bhawna Paliwal,Haritheja,Will Liang, Pieter Abbeel , Mahi Shafiullah 🏠🤖 , Jitendra MALIK
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We can convert human videos to robot hand-object interaction trajectories in 4D. Enjoy! Paper: Website: Code: Authors:Bhawna Paliwal,Haritheja,Will Liang, Pieter Abbeel , Mahi Shafiullah 🏠🤖 , Jitendra MALIK

Jitendra MALIK

57,729 просмотров • 8 дней назад

Shanghai’s Moya humanoid robot walks with human-like motion, shows realistic facial expressions, and is designed for natural interaction in healthcare and service roles.
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Shanghai’s Moya humanoid robot walks with human-like motion, shows realistic facial expressions, and is designed for natural interaction in healthcare and service roles.

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