Learning Continuous Grasp Affordances by Sensorimotor Exploration

  • R. Detry
  • E. Başeski
  • M. Popović
  • Y. Touati
  • N. Krüger
  • O. Kroemer
  • J. Peters
  • J. Piater
Part of the Studies in Computational Intelligence book series (SCI, volume 264)

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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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • R. Detry
    • 1
  • E. Başeski
    • 2
  • M. Popović
    • 2
  • Y. Touati
    • 2
  • N. Krüger
    • 2
  • O. Kroemer
    • 3
  • J. Peters
    • 3
  • J. Piater
    • 1
  1. 1.University of LiègeBelgium
  2. 2.University of SouthernDenmark
  3. 3.MPI for Biological CyberneticsTübingenGermany

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