Reinforcement Learning and Robust Control for Robot Compliance Tasks

Cheng Peng Kuan*, Kuu-Young Young

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

14 Scopus citations

Abstract

The complexity in planning and control of robot compliance tasks mainly results from simultaneous control of both position and force and inevitable contact with environments. It is quite difficult to achieve accurate modeling of the interaction between the robot and the environment during contact. In addition, the interaction with the environment varies even for compliance tasks of the same kind. To deal with these phenomena, in this paper, we propose a reinforcement learning and robust control scheme for robot compliance tasks. A reinforcement learning mechanism is used to tackle variations among compliance tasks of the same kind. A robust compliance controller that guarantees system stability in the presence of modeling uncertainties and external disturbances is used to execute control commands sent from the reinforcement learning mechanism. Simulations based on deburring compliance tasks demonstrate the effectiveness of the proposed scheme.

Original languageEnglish
Pages (from-to)165-182
Number of pages18
JournalJournal of Intelligent and Robotic Systems: Theory and Applications
Volume23
Issue number2-4
DOIs
StatePublished - Oct 1998

Keywords

  • Compliance tasks
  • Reinforcement learning
  • Robust control

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