Constrained-Space Optimization and Reinforcement Learning for Complex Tasks

Ya Yen Tsai*, Bo Xiao, Edward Johns, Guang Zhong Yang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

13 Citations (Scopus)

Abstract

Learning from demonstration is increasingly used for transferring operator manipulation skills to robots. In practice, it is important to cater for limited data and imperfect human demonstrations, as well as underlying safety constraints. This article presents a constrained-space optimization and reinforcement learning scheme for managing complex tasks. Through interactions within the constrained space, the reinforcement learning agent is trained to optimize the manipulation skills according to a defined reward function. After learning, the optimal policy is derived from the well-trained reinforcement learning agent, which is then implemented to guide the robot to conduct tasks that are similar to the experts' demonstrations. The effectiveness of the proposed method is verified with a robotic suturing task, demonstrating that the learned policy outperformed the experts' demonstrations in terms of the smoothness of the joint motion and end-effector trajectories, as well as the overall task completion time.

Original languageEnglish
Article number8954748
Pages (from-to)682-689
Number of pages8
JournalIEEE Robotics and Automation Letters
Volume5
Issue number2
DOIs
Publication statusPublished - Apr 2020
Externally publishedYes

Keywords

  • Learn from demonstration (LfD)
  • medical robotics
  • reinforcement learning (RL)
  • robot learning
  • robotic suturing

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