Reinforcement learning to adjust parametrized motor primitives to new situations

Name: Reinforcement learning to adjust parametrized motor primitives to new situations
Author: Kober, J., Wilhelm, A., Öztop, Erhan, Peters, J.

İsim	Reinforcement learning to adjust parametrized motor primitives to new situations
Yazar	Kober, J., Wilhelm, A., Öztop, Erhan, Peters, J.
Basım Tarihi:	2012-11
Basım Yeri	- Springer Science+Business Media
Konu	Skill learning, Motor primitives, Reinforcement learning, Meta-parameters, Policy learning
Tür	Süreli Yayın
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	1573-7527
Kayıt Numarası	1a72cfbd-4fb3-4174-8e3b-f2865b40443d
Lokasyon	Computer Science
Tarih	2012-11
Notlar	European Community
Örnek Metin	Humans manage to adapt learned movements very quickly to new situations by generalizing learned behaviors from similar situations. In contrast, robots currently often need to re-learn the complete movement. In this paper, we propose a method that learns to generalize parametrized motor plans by adapting a small set of global parameters, called meta-parameters. We employ reinforcement learning to learn the required meta-parameters to deal with the current situation, described by states. We introduce an appropriate reinforcement learning algorithm based on a kernelized version of the reward-weighted regression. To show its feasibility, we evaluate this algorithm on a toy example and compare it to several previous approaches. Subsequently, we apply the approach to three robot tasks, i.e., the generalization of throwing movements in darts, of hitting movements in table tennis, and of throwing balls where the tasks are learned on several different real physical robots, i.e., a Barrett WAM, a BioRob, the JST-ICORP/SARCOS CBi and a Kuka KR 6.
DOI	10.1007/s10514-012-9290-3
Cilt	33

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Reinforcement learning to adjust parametrized motor primitives to new situations

Yazar Kober, J., Wilhelm, A., Öztop, Erhan, Peters, J.

Basım Tarihi 2012-11

Basım Yeri - Springer Science+Business Media

Konu Skill learning, Motor primitives, Reinforcement learning, Meta-parameters, Policy learning

Tür Süreli Yayın

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 1573-7527

Kayıt Numarası 1a72cfbd-4fb3-4174-8e3b-f2865b40443d

Lokasyon Computer Science

Tarih 2012-11

Notlar European Community

Örnek Metin Humans manage to adapt learned movements very quickly to new situations by generalizing learned behaviors from similar situations. In contrast, robots currently often need to re-learn the complete movement. In this paper, we propose a method that learns to generalize parametrized motor plans by adapting a small set of global parameters, called meta-parameters. We employ reinforcement learning to learn the required meta-parameters to deal with the current situation, described by states. We introduce an appropriate reinforcement learning algorithm based on a kernelized version of the reward-weighted regression. To show its feasibility, we evaluate this algorithm on a toy example and compare it to several previous approaches. Subsequently, we apply the approach to three robot tasks, i.e., the generalization of throwing movements in darts, of hitting movements in table tennis, and of throwing balls where the tasks are learned on several different real physical robots, i.e., a Barrett WAM, a BioRob, the JST-ICORP/SARCOS CBi and a Kuka KR 6.

DOI 10.1007/s10514-012-9290-3

Cilt 33