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A Learning-Based Framework for Robot Peg-Hole-Insertion

[+] Author Affiliations
Te Tang, Hsien-Chung Lin, Masayoshi Tomizuka

University of California, Berkeley, CA

Paper No. DSCC2015-9703, pp. V002T27A002; 9 pages
doi:10.1115/DSCC2015-9703
From:
  • ASME 2015 Dynamic Systems and Control Conference
  • Volume 2: Diagnostics and Detection; Drilling; Dynamics and Control of Wind Energy Systems; Energy Harvesting; Estimation and Identification; Flexible and Smart Structure Control; Fuels Cells/Energy Storage; Human Robot Interaction; HVAC Building Energy Management; Industrial Applications; Intelligent Transportation Systems; Manufacturing; Mechatronics; Modelling and Validation; Motion and Vibration Control Applications
  • Columbus, Ohio, USA, October 28–30, 2015
  • Conference Sponsors: Dynamic Systems and Control Division
  • ISBN: 978-0-7918-5725-0
  • Copyright © 2015 by ASME

abstract

Peg-hole-insertion is a common operation in industry production, but autonomous execution by robots has been a big challenge for many years. Current robot programming for this kind of contact problem requires tremendous effort, which needs delicate trajectory and force tuning. However, human may accomplish this task with much less time and fewer trials. It will be a great benefit if robots can learn the human skill and apply it autonomously. This paper introduces a framework for teaching robot peg-hole-insertion from human demonstration. A Dimension Reduction and Recovery method is proposed to simplify control policy learning. The Gaussian Mixture Regression is utilized to imitate human skill and a Dual Stage Force Control strategy is designed for autonomous execution by robots. The effectiveness of the teaching framework is demonstrated by a series of experiments.

Copyright © 2015 by ASME
Topics: Robots

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