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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 Aufrufe • vor 9 Monaten •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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🎾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

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🤖 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 🔗:
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🤖 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 🔗:

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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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🤖 Introducing Human-Object Interaction from Human-Level Instructions! First complete system that generates physically plausible, long-horizon human-object interactions with finger motions in contextual environments, driven by human-level instructions. 🔍 Our approach: - LLMs transform instructions into execution plans & target layouts - Multi-stage framework generates synchronized object, body & finger motions - RL policy tracks generated motions in physics simulation Project: Demo: Paper:

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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.

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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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