  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.
