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Publication Details

Reference TypeConference Proceedings
Author(s)Paster, P.; Righetti, L.; Kalakrishnan, M.; Schaal, S.
TitleOnline movement adaptation based on previous sensor experiences
Journal/Conference/Book TitleProceedings of the International Conference of Intelligent Robots and Systems (IROS 2011)
Keywordsmovement primitives, associative skill memories, prediction, reactive control, overall best paper award
Abstractersonal robots can only become widespread if they are capable of safely operating among humans. In uncertain and highly dynamic environments such as human households, robots need to be able to instantly adapt their behavior to unforseen events. In this paper, we propose a general framework to achieve very contact-reactive motions for robotic grasping and manipulation. Associating stereotypical movements to particular tasks enables our system to use previous sensor experiences as a predictive model for subsequent task executions. We use dynamical systems, named Dynamic Movement Primitives (DMPs), to learn goal-directed behaviors from demonstration. We exploit their dynamic properties by coupling them with the measured and predicted sensor traces. This feedback loop allows for online adaptation of the movement plan. Our system can create a rich set of possible motions that account for external perturbations and perception uncertainty to generate truly robust behaviors. As an example, we present an application to grasping with the WAM robot arm.
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