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Learning Bayesian models of sensorimotor interaction: from random exploration toward the discovery of new behaviors

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3 Author(s)
Simonin, E. ; Lab. GRAVIR/IMAG, INRIA, Rhone-Alpes, France ; Diard, J. ; Bessiere, P.

We are interested in probabilistic models of space and navigation. We describe an experiment where a Koala robot uses experimental data, gathered by randomly exploring the sensorimotor space, so as to learn a model of its interaction with the environment. This model is then used to generate a variety of new behaviors, from obstacle avoidance to wall following to ball pushing, which were previously unknown by the robot. The learned model can be seen as a building block for a hierarchical control architecture based on the Bayesian map formalism.

Published in:

Intelligent Robots and Systems, 2005. (IROS 2005). 2005 IEEE/RSJ International Conference on

Date of Conference:

2-6 Aug. 2005

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