HMC：接触の多い移動操作のための異種メタ制御の学習
HMC: Learning Heterogeneous Meta-Control for Contact-Rich Loco-Manipulation
位置・インピーダンス・力制御をトルク空間で統合する枠組みを提案し、実機ヒューマノイドで接触を伴う拭き掃除や引き出し開けの成功率を大幅に改善した。
詳しい要約
1. どんなもの？
2. 先行研究と比べてどこがすごい？
3. 技術・手法の肝は？
4. どうやって有効だと検証した？
5. 議論はある？
6. 次に読むべき論文は？
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著者: Lai Wei, Xuanbin Peng, Ri-Zhao Qiu, Tianshu Huang, Xuxin Cheng, Xiaolong Wang
分類: cs.RO
原文アブストラクト
Learning from real-world robot demonstrations holds promise for interacting with complex real-world environments. However, the complexity and variability of interaction dynamics often cause purely positional controllers to struggle with contacts or varying payloads. To address this, we propose a Heterogeneous Meta-Control (HMC) framework for Loco-Manipulation that adaptively stitches multiple control modalities: position, impedance, and hybrid force-position. We first introduce an interface, HMC-Controller, for blending actions from different control profiles continuously in the torque space. HMC-Controller facilitates both teleoperation and policy deployment. Then, to learn a robust force-aware policy, we propose HMC-Policy to unify different controllers into a heterogeneous architecture. We adopt a mixture-of-experts style routing to learn from large-scale position-only data and fine-grained force-aware demonstrations. Experiments on a real humanoid robot show over 50% relative improvement vs. baselines on challenging tasks such as compliant table wiping and drawer opening, demonstrating the efficacy of HMC.
