Learning Motor Primitives for Robotics

Kober, J; Peters, J

Item

ITEM ACTIONSEXPORT

Add to Basket

Local TagsRelease HistoryDetailsSummary

Released

Talk

Learning Motor Primitives for Robotics

MPS-Authors

/persons/resource/persons84021

Kober, J
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

/persons/resource/persons84135

Peters, J
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

External Resource

No external resources are shared

Fulltext (restricted access)

There are currently no full texts shared for your IP range.

Fulltext (public)

There are no public fulltexts stored in PuRe

Supplementary Material (public)

There is no public supplementary material available

Citation

Kober, J., & Peters, J. (2010). Learning Motor Primitives for Robotics. Talk presented at EVENT Lab: Reinforcement Learning in Robotics and Virtual Reality. Barcelona, Spain. 2010-01-15.

Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-C15A-8

Abstract

The acquisition and self-improvement of novel motor skills is among the most important problems in robotics. Motor primitives offer one of the most promising frameworks for the application of machine learning techniques in this context. Employing the Dynamic Systems Motor primitives originally introduced by Ijspeert et al. (2003), appropriate learning algorithms for a concerted approach of both imitation and reinforcement learning are presented. Using these algorithms new motor skills, i.e., Ball-in-a-Cup, Ball-Paddling and Dart-Throwing, are learned.