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Paper TuA07.3

Bocsi, Botond (Babes Bolyai University), Hennig, Philipp (MPI Intelligent Systems), Csatů, Lehel (Babes Bolyai University), Peters, Jan (Technische Universitšt Darmstadt)

Learning Tracking Control with Forward Models

Scheduled for presentation during the Regular Session "Robust and Adaptive Control of Robotic Systems" (TuA07), Tuesday, May 15, 2012, 09:00−09:15, Meeting Room 7 (Remnicha)

2012 IEEE International Conference on Robotics and Automation, May 14-18, 2012, RiverCentre, Saint Paul, Minnesota, USA

This information is tentative and subject to change. Compiled on November 14, 2018

Keywords Robust/Adaptive Control of Robotic Systems, Flexible Arms, Learning and Adaptive Systems

Abstract

Performing task-space tracking control on redundant robot manipulators is a difficult problem. When the physical model of the robot is too complex or not available, standard methods fail and machine learning algorithms can have advantages. We propose an adaptive learning algorithm for tracking control of underactuated or non-rigid robots where the physical model of the robot is unavailable. The control method is based on the fact that forward models are relatively straightforward to learn and local inversions can be obtained via local optimization. We use sparse online Gaussian process inference to obtain a flexible probabilistic forward model and second order optimization to find the inverse mapping. Physical experiments indicate that this approach can outperform state-of-the-art tracking control algorithms in this context.

 

 

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