On-line motion synthesis and adaptation using a trajectory database

Authors:
Denis Forte;Andrej Gams;Jun Morimoto;Aleš Ude
Affiliations:
Joef Stefan Institute, Department of Automatics, Biocybernetics, and Robotics, Jamova cesta 39, 1000 Ljubljana, Slovenia;Joef Stefan Institute, Department of Automatics, Biocybernetics, and Robotics, Jamova cesta 39, 1000 Ljubljana, Slovenia;ATR Computational Neuroscience Laboratories, Department of Brain Robot Interface, 2-2-2 Hikaridai, Seika-cho, Soraku-gun, Kyoto 619-0288, Japan;Joef Stefan Institute, Department of Automatics, Biocybernetics, and Robotics, Jamova cesta 39, 1000 Ljubljana, Slovenia and ATR Computational Neuroscience Laboratories, Department of Brain Robot ...
Venue:
Robotics and Autonomous Systems
Year:
2012

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Abstract

Autonomous robots cannot be programmed in advance for all possible situations. Instead, they should be able to generalize the previously acquired knowledge to operate in new situations as they arise. A possible solution to the problem of generalization is to apply statistical methods that can generate useful robot responses in situations for which the robot has not been specifically instructed how to respond. In this paper we propose a methodology for the statistical generalization of the available sensorimotor knowledge in real-time. Example trajectories are generalized by applying Gaussian process regression, using the parameters describing a task as query points into the trajectory database. We show on real-world tasks that the proposed methodology can be integrated into a sensory feedback loop, where the generalization algorithm is applied in real-time to adapt robot motion to the perceived changes of the external world.