Please use this identifier to cite or link to this item:https://hdl.handle.net/20.500.12259/57276
Type of publication: research article
Type of publication (PDB): Straipsnis konferencijos medžiagoje kitose duomenų bazėse / Article in conference proceedings in other databases (P1c)
Field of Science: Informatika / Informatics (N009)
Author(s): Nemec, Bojan;Tamošiūnaitė, Minija;Wörgötter, Florentin
Title: Task adaptation through exploration and action sequencing
Is part of: Humanoids'09 : 9th IEEE-RAS international conference on Humanoid Robots, December 7-10, 2009 Paris, France. Piscataway, N.J. : IEEE Press, 2009
Extent: p. 610-616
Date: 2009
Note: ISBN 9781424445875 (online)
Keywords: Movement primitive;Reinforcement learning;Robot
ISBN: 9781424445974
Abstract: General-purpose autonomous robots need to have the ability to sequence and adapt the available sensorimotor knowledge, which is often given in the form of movement primitives. In order to solve a given task in situations that were not considered during the initial learning, it is necessary to adapt trajectories contained in the library of primitive motions to new situations. In this paper we explore how to apply reinforcement learning to modify the subgoals of primitive movements involved in the given task. As the underlying sensorimotor representation we selected nonlinear dynamic systems, which provide a powerful machinery for the modification of motion trajectories. We propose a new formulation for dynamic systems, which ensures that consecutive primitive movements can be splined together in a continuous way (up to second order derivatives)
Internet: https://hdl.handle.net/20.500.12259/57276
Affiliation(s): Taikomosios informatikos katedra
Vytauto Didžiojo universitetas
Appears in Collections:Universiteto mokslo publikacijos / University Research Publications

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