強化学習で適応化したモデル予測制御による不整地二足歩行
RL-augmented Adaptive Model Predictive Control for Bipedal Locomotion over Challenging Terrain
単剛体力学に基づくMPCの力学・遊脚制御・歩容周波数を強化学習でパラメータ化し、階段や滑りやすい不整地での二足歩行を頑健化した。
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著者: Junnosuke Kamohara, Feiyang Wu, Chinmayee Wamorkar, Seth Hutchinson, Ye Zhao
分類: cs.RO
原文アブストラクト
Model predictive control (MPC) has demonstrated effectiveness for humanoid bipedal locomotion; however, its applicability in challenging environments, such as rough and slippery terrain, is limited by the difficulty of modeling terrain interactions. In contrast, reinforcement learning (RL) has achieved notable success in training robust locomotion policies over diverse terrain, yet it lacks guarantees of constraint satisfaction and often requires substantial reward shaping. Recent efforts in combining MPC and RL have shown promise of taking the best of both worlds, but they are primarily restricted to flat terrain or quadrupedal robots. In this work, we propose an RL-augmented MPC framework tailored for bipedal locomotion over rough and slippery terrain. Our method parametrizes three key components of single-rigid-body-dynamics-based MPC: system dynamics, swing leg controller, and gait frequency. We validate our approach through bipedal robot simulations in NVIDIA IsaacLab across various terrains, including stairs, stepping stones, and low-friction surfaces. Experimental results demonstrate that our RL-augmented MPC framework produces significantly more adaptive and robust behaviors compared to baseline MPC and RL.
