Efficient Learning of Motion Patterns for Robots

Publisher:
Australian Robotics and Automation Association Inc.
Publication Type:
Conference Proceeding
Citation:
Proceedings of the 2009 Australasian Conference on Robotics and Automation, 2009, pp. 1 - 7
Issue Date:
2009-01
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In this work we present a novel approach to learning dynamics of an environment perceived by a mobile robot. More precisely, we are interested in general motion patterns occurring in the environment rather than object dependent ones. A sampling algorithm is used to update a sample set, which represents observed dynamics, using the Bayes rule. From this set of samples a Hidden Markov Model is learnt online, which allows fast and efficient matching and prediction in the learnt model. Such models are useful for a number of tasks such as path planning, localisation and compliant motion. The approach is validated through simulation as well as experiments.
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