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  Generalizing Demonstrated Actions in Manipulation Tasks

Kroemer, O., Detry R, Piater, J., & Peters, J. (2010). Generalizing Demonstrated Actions in Manipulation Tasks.

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Kroemer, O1, Autor           
Detry R, Piater, J1, Autor           
Peters, J1, 2, Autor           
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society, ou_1497647              

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 Zusammenfassung: Programming-by-demonstration promises to significantly reduce the burden of coding robots to perform new tasks. However, service robots will be presented with a variety of different situations that were not specifically demonstrated to it. In such cases, the robot must autonomously generalize its learned motions to these new situations. We propose a system that can generalize movements to new target locations and even new objects. The former is achieved by using a task-specific coordinate system together with dynamical systems motor primitives. Generalizing actions to new objects is a more complex problem, which we solve by treating it as a continuum-armed bandits problem. Using the bandits framework, we can efficiently optimize the learned action for a specific object. The proposed method was implemented on a real robot and succesfully adapted the grasping action to three different objects. Although we focus on grasping as an example of a task, the proposed methods are much more widely applicable to robot manipulation tasks.

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 Datum: 2010-10
 Publikationsstatus: Erschienen
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 Identifikatoren: URI: http://pages.isir.upmc.fr/iros10ws/
BibTex Citekey: 6852
 Art des Abschluß: -

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