COMPASS: 残差強化学習とスキル統合によるクロスエンボディメント移動ポリシー
COMPASS: Cross-embodiment Mobility Policy via Residual RL and Skill Synthesis
単一のロボットの模倣学習デモのみから出発し、残差強化学習で適応させ、エンボディメント埋め込みで一般化ポリシーに蒸留することで、多様な機体にまたがる移動を実現した研究。
著者: Wei Liu, Huihua Zhao, Chenran Li, Yuchen Deng, Joydeep Biswas, Soha Pouya, Yan Chang
分類: cs.RO
原文アブストラクト
As robots are increasingly deployed in diverse application domains, enabling robust mobility across different embodiments has become a critical challenge. Classical mobility stacks, though effective on specific platforms, require extensive per-robot tuning and do not scale easily to new embodiments. Learning-based approaches, such as imitation learning (IL), offer alternatives, but face significant limitations on the need for high-quality demonstrations for each embodiment. To address these challenges, we introduce COMPASS, a unified framework that enables scalable cross-embodiment mobility using expert demonstrations from only a single embodiment. We first pre-train a mobility policy on a single robot using IL, combining a world model with a policy model. We then apply residual reinforcement learning (RL) to efficiently adapt this policy to diverse embodiments through corrective refinements. Finally, we distill specialist policies into a single generalist policy conditioned on an embodiment embedding vector. This design significantly reduces the burden of collecting data while enabling robust generalization across a wide range of robot designs. Our experiments demonstrate that COMPASS scales effectively across diverse robot platforms while maintaining adaptability to various environment configurations, achieving a generalist policy with a success rate approximately 5X higher than the pre-trained IL policy on unseen embodiments, and further demonstrates zero-shot sim-to-real transfer.
