Generalization

Part-based Grasp Generalization

CogX Part of the EU project CogX.

TOMSY Part of the EU project TOMSY.

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We present a real-world robotic agent that is capable of transferring grasping strategies across objects that share similar parts. The agent transfers grasps across objects by identifying, from examples provided by a teacher, parts by which objects are often grasped in a similar fashion. It then uses these parts to identify grasping points onto novel objects. While prior work in this area focused primarily on shape analysis (parts identified, e.g., through visual clustering, or salient structure analysis), the key aspect of this work is the emergence of parts from both object shape and grasp examples. As a result, parts intrinsically encode the intention of executing a grasp.

We devise a similarity measure that reflects whether the shapes of two parts resemble each other, and whether their associated grasps are applied near one another. We discuss a nonlinear clustering procedure that allows groups of similar part-grasp associations to emerge from the space induced by the similarity measure. We present an experiment in which our agent extracts five prototypical parts from thirty-two grasp examples, and we demonstrate the applicability of the prototypical parts for grasping novel objects.

Video illustrating the part learning process. Download this video in MP4/H.264 or WebM/VP8, or view it on Youtube.

CogX Part of the EU project CogX.

TOMSY Part of the EU project TOMSY.

FNRS Supported by the Belgian National Fund for Scientific Research (FNRS).

SSF Supported by the Swedish Foundation for Strategic Research (SSF).

Nuklei Code based on the Nuklei library.

Main reference:

detry2013a 
R. Detry, C. H. Ek, M. Madry and D. Kragic, Learning a Dictionary of Prototypical Grasp-predicting Parts from Grasping Experience. In IEEE International Conference on Robotics and Automation, 2013.
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Papers covering this topic:

detry2010d 
R. Detry, Learning of Multi-Dimensional, Multi-Modal Features for Robotic Grasping. Ph.D. Thesis, University of Liège, 2010.
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detry2011b 
R. Detry and J. Piater, Grasp Generalization Via Predictive Parts. In Austrian Robotics Workshop, 2011.
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detry2012a 
R. Detry, C. H. Ek, M. Madry, J. Piater and D. Kragic, Generalizing Grasps Across Partly Similar Objects. In IEEE International Conference on Robotics and Automation, 2012.
doidoi; pdfpdf; bibtexshow/hide bibtex
detry2012b 
R. Detry, C. H. Ek, M. Madry and D. Kragic, Compressing Grasping Experience into a Dictionary of Prototypical Grasp-predicting Parts. In International Workshop on Human-Friendly Robotics, 2012.
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detry2013a 
R. Detry, C. H. Ek, M. Madry and D. Kragic, Learning a Dictionary of Prototypical Grasp-predicting Parts from Grasping Experience. In IEEE International Conference on Robotics and Automation, 2013.
doidoi; pdfpdf; bibtexshow/hide bibtex
hjelm2013a 
M. Hjelm, C. H. Ek, R. Detry, H. Kjellström and D. Kragic, Sparse Summarization of Robotic Grasping Data. In IEEE International Conference on Robotics and Automation, 2013.
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