AB  - 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.
AU  - Sehestedt, S
AU  - Kodagoda, S
AU  - Alempijevic, A
AU  - Dissanayake, G
DA  - 2009/12/01
JO  - Proceedings of the 2009 Australasian Conference on Robotics and Automation, ACRA 2009
PY  - 2009/12/01
TI  - Efficient learning of motion patterns for robots
Y1  - 2009/12/01
Y2  - 2026/06/21
ER  -