Crossing the Human-Robot Embodiment Gap with Sim-to-Real RL using One Human Demonstration

• URL: http://arxiv.org/abs/2504.12609v3
• Date: Sat, 16 Aug 2025 04:32:53 GMT
• Title: Crossing the Human-Robot Embodiment Gap with Sim-to-Real RL using One Human Demonstration
• Authors: Tyler Ga Wei Lum, Olivia Y. Lee, C. Karen Liu, Jeannette Bohg,
• Abstract summary: We propose a novel real-to-sim-to-real framework for training dexterous manipulation policies using only one RGB-D video of a human demonstrating a task.<n>Human2Sim2Robot outperforms object-aware replay by over 55% and imitation learning by over 68% on grasping, non-prehensile manipulation, and multi-step tasks.
• Score: 21.94699075066712
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Teaching robots dexterous manipulation skills often requires collecting hundreds of demonstrations using wearables or teleoperation, a process that is challenging to scale. Videos of human-object interactions are easier to collect and scale, but leveraging them directly for robot learning is difficult due to the lack of explicit action labels and human-robot embodiment differences. We propose Human2Sim2Robot, a novel real-to-sim-to-real framework for training dexterous manipulation policies using only one RGB-D video of a human demonstrating a task. Our method utilizes reinforcement learning (RL) in simulation to cross the embodiment gap without relying on wearables, teleoperation, or large-scale data collection. From the video, we extract: (1) the object pose trajectory to define an object-centric, embodiment-agnostic reward, and (2) the pre-manipulation hand pose to initialize and guide exploration during RL training. These components enable effective policy learning without any task-specific reward tuning. In the single human demo regime, Human2Sim2Robot outperforms object-aware replay by over 55% and imitation learning by over 68% on grasping, non-prehensile manipulation, and multi-step tasks. Website: https://human2sim2robot.github.io

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