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Paper FrB6.2

Kober, Jens (Max-Planck-Institute for Biological Cybernetics), Peters, Jan (Max-Planck Inst. for Bio. Cybernetics)

Learning Motor Primitives for Robotics

Scheduled for presentation during the Regular Sessions "Learning and Adaptive Systems - II" (FrB6), Friday, May 15, 2009, 10:50−11:10, Room: 404

2009 IEEE International Conference on Robotics and Automation, May 12 - 17, 2009, Kobe, Japan

This information is tentative and subject to change. Compiled on January 24, 2022

Keywords Learning and Adaptive Systems

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 an improved form of the Dynamic Systems Motor primitives originally introduced by Ijspeert et al. (2003), we show how both discrete and rhythmic tasks can be learned using a concerted approach of both imitation and reinforcement learning. For doing so, we present both learning algorithms and representations targeted for the practical application in robotics. We show that two new motor skills, i.e., Ball-In-A-Cup and Ball-Paddling, can be learned on a real Barrett WAM robot arm at a pace similar to human learning while achieving a significantly more reliable final performance.

 

 

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