English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Achieving stability and robustness is the primary goal of biped locomotioncontrol. Recently, deep reinforce learning (DRL) has attracted great attentionas a general methodology for constructing biped control policies anddemonstrated significant improvements over the previous state-of-the-art.Although deep control policies have advantages over previous controller designapproaches, many questions remain unanswered. Are deep control policies asrobust as human walking? Does simulated walking use similar strategies as humanwalking to maintain balance? Does a particular gait pattern similarly affecthuman and simulated walking? What do deep policies learn to achieve improvedgait stability? The goal of this study is to answer these questions byevaluating the push-recovery stability of deep policies compared to humansubjects and a previous feedback controller. We also conducted experiments toevaluate the effectiveness of variants of DRL algorithms.

Understanding the Stability of Deep Control Policies for Biped Locomotion