Impedance learning for robotic contact tasks using natural actor-critic algorithm

Byungchan Kim, Jooyoung Park, Shinsuk Park, Sungchul Kang

Research output: Contribution to journalArticle

57 Citations (Scopus)

Abstract

Compared with their robotic counterparts, humans excel at various tasks by using their ability to adaptively modulate arm impedance parameters. This ability allows us to successfully perform contact tasks even in uncertain environments. This paper considers a learning strategy of motor skill for robotic contact tasks based on a human motor control theory and machine learning schemes. Our robot learning method employs impedance control based on the equilibrium point control theory and reinforcement learning to determine the impedance parameters for contact tasks. A recursive least-square filter-based episodic natural actor-critic algorithm is used to find the optimal impedance parameters. The effectiveness of the proposed method was tested through dynamic simulations of various contact tasks. The simulation results demonstrated that the proposed method optimizes the performance of the contact tasks in uncertain conditions of the environment.

Original languageEnglish
Article number5204203
Pages (from-to)433-443
Number of pages11
JournalIEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Volume40
Issue number2
DOIs
Publication statusPublished - 2010 Apr

Keywords

  • Contact task
  • Equilibrium point control
  • Reinforcement learning
  • Robot manipulation

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Information Systems
  • Human-Computer Interaction
  • Computer Science Applications
  • Electrical and Electronic Engineering

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