نویسنده
Kober, J., Wilhelm, A., Öztop, Erhan, Peters, J.
تاریخ انتشار
2012-11
محل انتشار
-
Springer Science+Business Media
موضوع
Skill learning, Motor primitives, Reinforcement learning, Meta-parameters, Policy learning
نوع
دوره ای
زبان
انگلیسی
دیجیتال
بله
نسخه خطی
خیر
کتابخانه
دانشگاه اوزیغین
شناسه دارایی کتابخانه
1573-7527
شماره ثبت
1a72cfbd-4fb3-4174-8e3b-f2865b40443d
محل کتابخانه
Computer Science
تاریخ
2012-11
یادداشتها
European Community
متن نمونه
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
