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.
| Author(s): | Kober, J. and Peters, J. |
| Year: | 2010 |
| Month: | January |
| Day: | 15 |
| Bibtex Type: | Talk (talk) |
| Digital: | 0 |
| Electronic Archiving: | grant_archive |
| Event Name: | EVENT Lab: Reinforcement Learning in Robotics and Virtual Reality |
| Event Place: | Barcelona, Spain |
| Language: | en |
| Organization: | Max-Planck-Gesellschaft |
| School: | Biologische Kybernetik |
BibTex
@talk{6254,
title = {Learning Motor Primitives for Robotics},
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.},
organization = {Max-Planck-Gesellschaft},
school = {Biologische Kybernetik},
month = jan,
year = {2010},
slug = {6254},
author = {Kober, J. and Peters, J.},
month_numeric = {1}
}