期刊
IEEE-ASME TRANSACTIONS ON MECHATRONICS
卷 27, 期 5, 页码 3132-3143出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TMECH.2021.3110825
关键词
Assembly skill; impedance control; learning from demonstration; movement primitives (MPs)
类别
资金
- Beijing Natural Science Foundation [L201019]
- National Natural Science Foundation of China [91848109]
- Major Scientific and Technological Innovation Projects in Shandong Province [2019JZZY010430]
- Technology on Space Intelligent Control Laboratory [HTKJ2019KL502013]
This article introduces a method to teach a robot assembly skills using learning from demonstration, training both trajectory and insertion force simultaneously. By encoding human demonstrations data via motion primitives and using an adaptive impedance controller to track prescribed force, the proposed approach combines adaptive impedance control techniques with learning from demonstration, proving efficient in experiments on several typical noncylindrical parts.
The purpose of this article is to teach a robot assembly skills from demonstrations, and we attempt to train both the trajectory and the insertion force simultaneously. We encode human demonstrations data via motion primitives and, then, generate a reference trajectory and a prescribed force profile for a new assembly task using the combination of the motion primitives. We then propose an adaptive impedance controller to track the prescribed force with unknown environment stiffness, where the impedance parameters are estimated by the optimal solution of an equivalent linearization model. Our approach combines adaptive impedance control techniques with learning from demonstration on the same that makes it tractable and applicable to a real robot. Experiments on several typical noncylindrical parts illustrate the efficiency of the proposed method.
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