X-Reset: クロスエンボディメント・リセットによる物体中心強化学習のスケーリング
X-Reset: Scaling Object-Centric Reinforcement Learning via Cross-Embodiment Resets
人間の手と物体のデモンストレーションをロボットのリセット状態に変換し、汎用物体中心報酬での強化学習の探索問題を解決するフレームワークを提案。
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著者: Prithwish Dan, Chenyang Ma, Wei Zhan
分類: cs.LG, cs.AI, cs.RO
原文アブストラクト
Reinforcement learning (RL) in simulation can train dexterous manipulation policies without robot demonstrations, but training a single generalist policy with task-agnostic rewards faces a severe exploration problem: approaching, grasping, and reorienting diverse objects with many degrees of freedom is difficult to discover from scratch. Prior works make exploration tractable with high-quality robot demonstrations, per-task reward shaping, or by restricting policies to narrow modes of behavior. We propose X-Reset, a framework that instead resolves exploration with human hand-object demonstrations. Rather than imitating or tracking retargeted human motion, X-Reset kinematically retargets hand-object states to noisy robot states, filters out states that are unstable in simulation, and samples the remainder as resets during RL training with general-purpose object-centric rewards. The resulting policy depends only on object state and goal, with demonstrations entering training through the reset distribution. We show that X-Reset trains generalist policies on 20 objects across three embodiments---a 22-DoF hand on two different arms and a parallel-jaw gripper---and resolves the exploration challenges of RL from scratch. X-Reset scales with the number of training objects, generalizes to unseen objects, can learn from imperfect hand-pose estimates, and transfers behaviors zero-shot from sim-to-real.