ReCo: 応答一貫性のある脚式マニピュレーションのためのポリシー認識型MPC
ReCo: Response-Consistent Locomotion with Policy-Aware MPC for Legged Manipulation
脚式ロボットの歩行中に腕で連続的なマニピュレーションを行うため、強化学習ポリシーの応答を一貫化し、その閉ループ応答モデルを組み込んだMPCでベースと腕を協調制御する手法を提案。
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著者: Kuankuan Sima, Yichao Gao, Chenxi Gu, Kefan Zhao, Lin Zhao
分類: cs.RO
原文アブストラクト
Continuous legged manipulation requires accurate end-effector tracking while the base keeps walking. Combining reinforcement learning (RL) with model predictive control (MPC) suits this task: the learned policy provides robust locomotion, while MPC coordinates the base and arm to compensate for tracking errors. However, MPC can compensate only for base motion that it can predict, and a learned policy's command response varies with gait phase, contact, and payload. We present ReCo, a framework that couples response-consistent locomotion with policy-aware MPC for legged manipulation. Response shaping trains the policy to respond to commands consistently and repeatably across randomized dynamics. An identified closed-loop response model then lets MPC jointly plan locomotion commands and arm motion. On the simulation benchmark, ReCo reduces position and orientation root-mean-square error (RMSE) by 28.7% and 27.4% relative to the best baseline for each metric. Real-world experiments demonstrate onboard continuous legged manipulation with coordinated base and arm motion.
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