Reinforcement Learning-based Sim-to-Real Impedance Parameter Tuning for Robotic Assembly

Yong Geon Kim, Minwoo Na, Jae Bok Song

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

Abstract

When performing robotic assembly, a task should be conducted through force-based control such as impedance control. Using impedance control, it is possible to control the contact force by appropriately adjusting the impedance parameters. However, the impedance parameters should be set by the user because it is difficult to accurately recognize the dynamics of the contact environment, which takes a lot of time because it should be performed whenever the assembly task changes. Moreover, the parameters may not be optimal because it depends on the experience and skill level of the user. To this end, a reinforcement learning-based impedance parameter tuning method is proposed in this study. Since this method uses only the physics-based robotic simulation on the virtual environment, there is no risk of damaging the robots or parts and learning time can be significantly reduced. The proposed method was verified by assembling an HDMI connector with a tolerance of 0.03 mm. Impedance parameters were learned in the virtual environment and transferred to the real environment. Finally, it was confirmed that parameter tuning for impedance without the aid of the user is possible by using the proposed method.

Original languageEnglish
Title of host publication2021 21st International Conference on Control, Automation and Systems, ICCAS 2021
PublisherIEEE Computer Society
Pages833-836
Number of pages4
ISBN (Electronic)9788993215212
DOIs
Publication statusPublished - 2021
Event21st International Conference on Control, Automation and Systems, ICCAS 2021 - Jeju, Korea, Republic of
Duration: 2021 Oct 122021 Oct 15

Publication series

NameInternational Conference on Control, Automation and Systems
Volume2021-October
ISSN (Print)1598-7833

Conference

Conference21st International Conference on Control, Automation and Systems, ICCAS 2021
Country/TerritoryKorea, Republic of
CityJeju
Period21/10/1221/10/15

Keywords

  • Connector assembly
  • Reinforcement learning
  • Robotic assembly

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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