ベイズ最適化とハイブリッドゼロダイナミクスを組み合わせた二足歩行制御の安全なパラメータ学習
Bayesian Optimization Meets Hybrid Zero Dynamics: Safe Parameter Learning for Bipedal Locomotion Control
ベイズ最適化とHZDを組み合わせ、シミュレーションと実機の両方で二足歩行ロボットの制御パラメータを安全制約付きで学習する枠組みを提案し、Cassieで歩行の滑らかさと追従誤差の改善を実証した。
著者: Lizhi Yang, Zhongyu Li, Jun Zeng, Koushil Sreenath
分類: cs.RO, cs.AI, cs.SY, eess.SY
原文アブストラクト
In this paper, we propose a multi-domain control parameter learning framework that combines Bayesian Optimization (BO) and Hybrid Zero Dynamics (HZD) for locomotion control of bipedal robots. We leverage BO to learn the control parameters used in the HZD-based controller. The learning process is firstly deployed in simulation to optimize different control parameters for a large repertoire of gaits. Next, to tackle the discrepancy between the simulation and the real world, the learning process is applied on the physical robot to learn for corrections to the control parameters learned in simulation while also respecting a safety constraint for gait stability. This method empowers an efficient sim-to-real transition with a small number of samples in the real world, and does not require a valid controller to initialize the training in simulation. Our proposed learning framework is experimentally deployed and validated on a bipedal robot Cassie to perform versatile locomotion skills with improved performance on smoothness of walking gaits and reduction of steady-state tracking errors.
