Learning Force-Relevant Skills from Human Demonstration

Xiao Gao, Jie Ling, Xiaohui Xiao and Miao Li

Complexity, 2019, vol. 2019, 1-11

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.

Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:hin:complx:5262859

DOI: 10.1155/2019/5262859

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