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Learning task-space tracking control with kernels

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2 Author(s)
Duy Nguyen-Tuong ; Max Planck Institute for Intelligent Systems, Spemannstraße 38, 72076 Tübingen, Germany ; Jan Peters

Task-space tracking control is essential for robot manipulation. In practice, task-space control of redundant robot systems is known to be susceptive to modeling errors. Here, data driven learning methods may present an interesting alternative approach. However, learning models for task-space tracking control from sampled data is an ill-posed problem. In particular, the same input data point can yield many different output values which can form a non-convex solution space. Because the problem is ill-posed, models cannot be learned from such data using common regression methods. While learning of task-space control mappings is globally ill-posed, it has been shown in recent work that it is locally a well-defined problem. In this paper, we use this insight to formulate a local kernel-based learning approach for online model learning for task-space tracking control. For evaluations, we show in simulation the ability of the method for online model learning for task-space tracking control of redundant robots.

Published in:

2011 IEEE/RSJ International Conference on Intelligent Robots and Systems

Date of Conference:

25-30 Sept. 2011