Understanding the Stability of Deep Control Policies for Biped Locomotion

Understanding the Stability of Deep Control Policies for Biped Locomotion

Hwangpil Park1    Ri Yu1    Yoonsang Lee2    Kyungho Lee3    Jehee Lee1


1 Seoul National University 2 Hanyang University 3 NC Soft



 
Abstract  

Achieving stability and robustness is the primary goal of biped locomotion control. Recently, deep reinforce learning (DRL) has attracted great attention as a general methodology for constructing biped control policies and demonstrated significant improvements over the previous state-of-the-art. Although deep control policies have advantages over previous controller design approaches, many questions remain unanswered. Are deep control policies as robust as human walking? Does simulated walking use similar strategies as human walking to maintain balance? Does a particular gait pattern similarly affect human and simulated walking? What do deep policies learn to achieve improved gait stability? The goal of this study is to answer these questions by evaluating the push-recovery stability of deep policies compared to human subjects and a previous feedback controller. We also conducted experiments to evaluate the effectiveness of variants of DRL algorithms.


Publication    

Hwangpil Park, Ri Yu, Yoonsang Lee, Kyungho Lee and Jehee Lee.
Understanding the Stability of Deep Control Policies for Biped Locomotion.
arXiv:2007.15242.
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