HiWET: 長期的なヒューマノイド移動操作のための階層型ワールドフレームエンドエフェクタ追従
HiWET: Hierarchical World-Frame End-Effector Tracking for Long-Horizon Humanoid Loco-Manipulation
ヒューマノイドの移動操作をワールド座標系でのエンドエフェクタ追従問題として再定式化し、高レベル方策がサブゴールを生成、低レベル方策が安定性制約下で実行する階層型強化学習フレームワークを提案。運動学的マニフォールド事前分布により探索を効率化し、実機へのゼロショット転移も実証した。
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著者: Zhanxiang Cao, Liyun Yan, Yang Zhang, Sirui Chen, Jianming Ma, Tianyue Zhan, Shengcheng Fu, Yufei Jia, Cewu Lu, Yue Gao
分類: cs.RO
原文アブストラクト
Humanoid loco-manipulation requires executing precise manipulation tasks while maintaining dynamic stability amid base motion and impacts. Existing approaches typically formulate commands in body-centric frames, fail to inherently correct cumulative world-frame drift induced by legged locomotion. We reformulate the problem as world-frame end-effector tracking and propose HiWET, a hierarchical reinforcement learning framework that decouples global reasoning from dynamic execution. The high-level policy generates subgoals that jointly optimize end-effector accuracy and base positioning in the world frame, while the low-level policy executes these commands under stability constraints. We introduce a Kinematic Manifold Prior (KMP) that embeds the manipulation manifold into the action space via residual learning, reducing exploration dimensionality and mitigating kinematically invalid behaviors. Extensive simulation and ablation studies demonstrate that HiWET achieves precise and stable end-effector tracking in long-horizon world-frame tasks. We validate zero-shot sim-to-real transfer of the low-level policy on a physical humanoid, demonstrating stable locomotion under diverse manipulation commands. These results indicate that explicit world-frame reasoning combined with hierarchical control provides an effective and scalable solution for long-horizon humanoid loco-manipulation.
