Emergence of Human-comparable Balancing Behaviors by Deep Reinforcement Learning

Chuanyu Yang, Taku Komura, Zhibin Li

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

This paper presents a hierarchical framework based on deep reinforcement learning that learns a diversity of policies for humanoid balance control. conventional zero moment point based controllers perform limited actions during under-actuation, whereas the proposed framework can perform human-like balancing behaviors such as active push-off of ankles. The learning is done through the design of an explainable reward based on physical constraints. The simulated results are presented and analyzed. The successful emergence of humanlike behaviors through deep reinforcement learning proves the feasibility of using an AI-based approach for learning humanoid balancing control in a unified framework.
Original languageEnglish
Title of host publication2017 IEEE-RAS International Conference on Humanoid Robots
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages8
ISBN (Electronic)978-1-5386-4678-6
ISBN (Print)978-1-5386-4679-3
Publication statusPublished - 8 Jan 2018
EventIEEE-RAS International Conference on Humanoid Robots - The Birmingham Repertory Theatre, Centenary Square, Broad Street, Birmingham, United Kingdom
Duration: 15 Nov 201717 Nov 2017

Publication series

ISSN (Electronic)2164-0580


ConferenceIEEE-RAS International Conference on Humanoid Robots
Country/TerritoryUnited Kingdom
Internet address


Dive into the research topics of 'Emergence of Human-comparable Balancing Behaviors by Deep Reinforcement Learning'. Together they form a unique fingerprint.

Cite this