NDLI: Learning adaptive movements from demonstration and self-guided exploration

Content Provider	IEEE Xplore Digital Library
Author	Bruno, D. Calinon, S. Caldwell, D.G.
Copyright Year	2014
Description	Author affiliation: Idiap Res. Inst., Martigny, Switzerland (Calinon, S.) \|\| Dept. of Adv. Robot., Ist. Italiano di Tecnol. (IIT), Genoa, Italy (Bruno, D.; Caldwell, D.G.)
Abstract	The combination of imitation and exploration strategies is used in this paper to transfer sensory-motor skills to robotic platforms. The aim is to be able to learn very different tasks with good generalization capabilities and starting from a few demonstrations. This goal is achieved by learning a task-parameterized model from demonstrations where a teacher shows the task corresponding to different possible values of preassigned parameters. In this manner, new reproductions can be generated for new situations by assigning new values to the parameters, thus achieving very precise generalization capabilities. In this paper we propose a novel algorithm that is able to learn the model together with its dependence from the task-parameters, without specifying a predefined relationship or structure. The algorithm is able to learn the model starting from a few demonstrations by applying an exploration strategy that refines the learnt model autonomously. The algorithm is tested on a reaching task performed with a Barrett WAM manipulator.
Starting Page	101
Ending Page	106
File Size	1081986
Page Count	6
File Format	PDF
ISBN	9781479975402
DOI	10.1109/DEVLRN.2014.6982961
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-10-13
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Tensile stress Prediction algorithms Robot sensing systems Vectors Trajectory Joints
Content Type	Text
Resource Type	Article

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