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Intentional Motion On-line Learning and Prediction

Dizan Alejandro Vasquez Govea 1 Thierry Fraichard 1 Olivier Aycard 1 Christian Laugier 1 
1 E-MOTION - Geometry and Probability for Motion and Action
GRAVIR - IMAG - Laboratoire d'informatique GRAphique, VIsion et Robotique de Grenoble, Inria Grenoble - Rhône-Alpes
Abstract : Motion prediction for objects which are able to decide their trajectory on the basis of a planning or decision process (e.g. humans and robots) is a challenging problem. Most existing approaches operate in two stages: a) learning, which consists in observing the environment in order to identify and model possible motion patterns or plans and b) prediction, which uses the learned plans in order to predict future motions. In existing techniques, learning is performed on-line, hence, it is impossible to rene the existing knowledge on the basis of the new observations obtained during the prediction phase. This paper proposes a novel learning approach which represents plans as Hidden Markov Models and is able to estimate the parameters and structure of those models in an incremental fashion by using the Growing Neural Gas algorithm. Our experiments demonstrate that the technique works in real-time, is able to operate concurrently with prediction and that the resulting model produces long-term predictions.
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Submitted on : Wednesday, October 24, 2007 - 6:34:19 PM
Last modification on : Wednesday, February 2, 2022 - 3:58:22 PM
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  • HAL Id : inria-00182039, version 1



Dizan Alejandro Vasquez Govea, Thierry Fraichard, Olivier Aycard, Christian Laugier. Intentional Motion On-line Learning and Prediction. Proc. of the Int. Conf. on Field and Service Robotics, Jul 2005, Port Douglas, Australia. ⟨inria-00182039⟩



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