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Speed adaptation for self-improvement of skills learned from user demonstrations

Published online by Cambridge University Press:  15 June 2015

Rok Vuga ,
Bojan Nemec  and
Aleš Ude
Rok Vuga*
Affiliation:
Humanoid and Cognitive Robotics Lab, Department of Automatics, Biocybernetics and Robotics, Jožef Stean Institute, Jamova cesta 39, 1000 Ljubljana, Slovenia. E-mails: [email protected], [email protected]
Bojan Nemec
Affiliation:
Humanoid and Cognitive Robotics Lab, Department of Automatics, Biocybernetics and Robotics, Jožef Stean Institute, Jamova cesta 39, 1000 Ljubljana, Slovenia. E-mails: [email protected], [email protected]
Aleš Ude
Affiliation:
Humanoid and Cognitive Robotics Lab, Department of Automatics, Biocybernetics and Robotics, Jožef Stean Institute, Jamova cesta 39, 1000 Ljubljana, Slovenia. E-mails: [email protected], [email protected]
*
*Corresponding author. E-mail: [email protected]
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Summary

The paper addresses the problem of speed adaptation of movements subject to environmental constraints. Our approach relies on a novel formulation of velocity profiles as an extension of dynamic movement primitives (DMP). The framework allows for compact representation of non-uniformly accelerated motion as well as simple modulation of the movement parameters. In the paper, we evaluate two model free methods by which optimal parameters can be obtained: iterative learning control (ILC) and policy search based reinforcement learning (RL). The applicability of each method is discussed and evaluated on two distinct cases, which are hard to model using standard techniques. The first deals with hard contacts with the environment while the second process involves liquid dynamics. We find ILC to be very efficient in cases where task parameters can be easily described with an error function. On the other hand, RL has stronger convergence properties and can therefore provide a solution in the general case.

Type
Articles
Information
Robotica , Volume 34 , Issue 12 , December 2016 , pp. 2806 - 2822
Copyright
Copyright © Cambridge University Press 2015 

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Footnotes

†

Initial results on the topic were presented at the IEEE-RAS International Conference on Humanoid Robots (Humanoids 2013), Atlanta, Georgia.1

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