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Learning Force-Relevant Skills from Human Demonstration

Author

Listed:
  • Xiao Gao
  • Jie Ling
  • Xiaohui Xiao
  • Miao Li

Abstract

Many human manipulation skills are force relevant, such as opening a bottle cap and assembling furniture. However, it is still a difficult task to endow a robot with these skills, which largely is due to the complexity of the representation and planning of these skills. This paper presents a learning-based approach of transferring force-relevant skills from human demonstration to a robot. First, the force-relevant skill is encapsulated as a statistical model where the key parameters are learned from the demonstrated data (motion, force). Second, based on the learned skill model, a task planner is devised which specifies the motion and/or the force profile for a given manipulation task. Finally, the learned skill model is further integrated with an adaptive controller that offers task-consistent force adaptation during online executions. The effectiveness of the proposed approach is validated with two experiments, i.e., an object polishing task and a peg-in-hole assembly.

Suggested Citation

  • Xiao Gao & Jie Ling & Xiaohui Xiao & Miao Li, 2019. "Learning Force-Relevant Skills from Human Demonstration," Complexity, Hindawi, vol. 2019, pages 1-11, February.
    • Handle: RePEc:hin:complx:5262859
    DOI: 10.1155/2019/5262859
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    1. Volodymyr Mnih & Koray Kavukcuoglu & David Silver & Andrei A. Rusu & Joel Veness & Marc G. Bellemare & Alex Graves & Martin Riedmiller & Andreas K. Fidjeland & Georg Ostrovski & Stig Petersen & Charle, 2015. "Human-level control through deep reinforcement learning," Nature, Nature, vol. 518(7540), pages 529-533, February.
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