Push recovery and active balancing for inexpensive humanoid robots using rl and drl

Amirhossein Hosseinmemar, John Anderson, Jacky Baltes, Meng Cheng Lau, Ziang Wang

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

Abstract

Push recovery of a humanoid robot is a challenging task because of many different levels of control and behaviour, from walking gait to dynamic balancing. This research focuses on the active balancing and push recovery problems that allow inexpensive humanoid robots to balance while standing and walking, and to compensate for external forces. In this research, we have proposed a push recovery mechanism that employs two machine learning techniques, Reinforcement Learning and Deep Reinforcement Learning, to learn recovery step trajectories during push recovery using a closed-loop feedback control. We have implemented a 3D model using the Robot Operating System and Gazebo. To reduce wear and tear on the real robot, we used this model for learning the recovery steps for different impact strengths and directions. We evaluated our approach in both in the real world and in simulation. All the real world experiments are performed by Polaris, a teen-sized humanoid robot.

Original languageEnglish
Title of host publicationTrends in Artificial Intelligence Theory and Applications. Artificial Intelligence Practices - 33rd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2020, Proceedings
EditorsHamido Fujita, Jun Sasaki, Philippe Fournier-Viger, Moonis Ali
PublisherSpringer Science and Business Media Deutschland GmbH
Pages63-74
Number of pages12
ISBN (Print)9783030557881
DOIs
Publication statusPublished - 2020
Event33rd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2020 - Kitakyushu, Japan
Duration: 2020 Sep 222020 Sep 25

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12144 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference33rd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2020
CountryJapan
CityKitakyushu
Period20/9/2220/9/25

Keywords

  • Active balancing
  • Deep reinforcement learning
  • Inexpensive humanoid robots
  • Push recovery
  • Reinforcement learning

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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