Learning robot stiffness for contact tasks using the natural actor-critic

Byungchan Kim, Byungduk Kang, Shinsuk Park, Sungchul Kang

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

3 Citations (Scopus)

Abstract

This paper introduces a novel motor learning strategy for robotic contact task based on a human motor control theory and machine learning schemes. Humans modulate their arm joint impedance parameters during contact tasks, and such aspect suggests a key feature how human successfully executes various contact tasks in variable environments. Our strategy for successful contact tasks is to find appropriate impedance parameters for optimal task execution by Reinforcement Learning (RL). In this study Recursive Least-Square (RLS) filter based episodic Natural Actor-Critic is employed to determine the optimal impedance parameters. Through dynamic simulations of contact tasks, this paper demonstrates the effectiveness of the proposed strategy. The simulation results show that the proposed method successfully optimizes the performance of the contact task and adapts to uncertain conditions of the environment.

Original languageEnglish
Title of host publication2008 IEEE International Conference on Robotics and Automation, ICRA 2008
Pages3832-3837
Number of pages6
DOIs
Publication statusPublished - 2008
Event2008 IEEE International Conference on Robotics and Automation, ICRA 2008 - Pasadena, CA, United States
Duration: 2008 May 192008 May 23

Publication series

NameProceedings - IEEE International Conference on Robotics and Automation
ISSN (Print)1050-4729

Other

Other2008 IEEE International Conference on Robotics and Automation, ICRA 2008
CountryUnited States
CityPasadena, CA
Period08/5/1908/5/23

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Artificial Intelligence
  • Electrical and Electronic Engineering

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  • Cite this

    Kim, B., Kang, B., Park, S., & Kang, S. (2008). Learning robot stiffness for contact tasks using the natural actor-critic. In 2008 IEEE International Conference on Robotics and Automation, ICRA 2008 (pp. 3832-3837). [4543799] (Proceedings - IEEE International Conference on Robotics and Automation). https://doi.org/10.1109/ROBOT.2008.4543799