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  Learning Continuous Grasp Affordances by Sensorimotor Exploration

Detry, R., Başeski, E., Popović, M., Touati, Y., Krüger, N., Krömer, O., et al. (2010). Learning Continuous Grasp Affordances by Sensorimotor Exploration. In O. Sigaud, & J. Peters (Eds.), From Motor Learning to Interaction Learning in Robots (pp. 451-465). Berlin, Germany: Springer.

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 Creators:
Detry, R, Author
Başeski, E, Author
Popović, M, Author
Touati, Y, Author
Krüger, N, Author
Krömer, O1, 2, Author              
Peters, J1, 2, Author              
Piater, J, Author              
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: We develop means of learning and representing object grasp affordances probabilistically. By grasp affordance, we refer to an entity that is able to assess whether a given relative object-gripper configuration will yield a stable grasp. These affordances are represented with grasp densities, continuous probability density functions defined on the space of 3D positions and orientations. Grasp densities are registered with a visual model of the object they characterize. They are exploited by aligning them to a target object using visual pose estimation. Grasp densities are refined through experience: A robot “plays” with an object by executing grasps drawn randomly for the object’s grasp density. The robot then uses the outcomes of these grasps to build a richer density through an importance sampling mechanism. Initial grasp densities, called hypothesis densities, are bootstrapped from grasps collected using a motion capture system, or from grasps generated from the visual model of the object. Refined densities, called empirical densities, represent affordances that have been confirmed through physical experience. The applicability of our method is demonstrated by producing empirical densities for two object with a real robot and its 3-finger hand. Hypothesis densities are created from visual cues and human demonstration.

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 Dates: 2010-01
 Publication Status: Published in print
 Pages: -
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 Rev. Type: -
 Identifiers: DOI: 10.1007/978-3-642-05181-4_19
BibTex Citekey: 6621
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Title: From Motor Learning to Interaction Learning in Robots
Source Genre: Book
 Creator(s):
Sigaud, O, Editor
Peters, J1, 2, Editor            
Affiliations:
1 Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795            
2 Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794            
Publ. Info: Berlin, Germany : Springer
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 451 - 465 Identifier: ISBN: 978-3-642-05181-4

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Title: Studies in Computational Intelligence
Source Genre: Series
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Pages: - Volume / Issue: 264 Sequence Number: - Start / End Page: - Identifier: -