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Learning object-specific grasp affordance densities

2009

Conference Paper

ei


This paper addresses the issue of learning and representing object grasp affordances, i.e. object-gripper relative configurations that lead to successful grasps. The purpose of grasp affordances is to organize and store the whole knowledge that an agent has about the grasping of an object, in order to facilitate reasoning on grasping solutions and their achievability. The affordance representation consists in a continuous probability density function defined on the 6D gripper pose space-3D position and orientation-, within an object-relative reference frame. Grasp affordances are initially learned from various sources, e.g. from imitation or from visual cues, leading to grasp hypothesis densities. Grasp densities are attached to a learned 3D visual object model, and pose estimation of the visual model allows a robotic agent to execute samples from a grasp hypothesis density under various object poses. Grasp outcomes are used to learn grasp empirical densities, i.e. grasps that have been confirmed through experience. We show the result of learning grasp hypothesis densities from both imitation and visual cues, and present grasp empirical densities learned from physical experience by a robot.

Author(s): Detry, R. and Baseski, E. and Popovic, M. and Touati, Y. and Krüger, N. and Kroemer, O. and Peters, J. and Piater, J.
Book Title: 8th IEEE International Conference on Development and Learning
Journal: Proceedings of the 8th IEEE International Conference on Development and Learning (ICDL 2009)
Pages: 1-7
Year: 2009
Month: June
Day: 0
Publisher: IEEE Service Center

Department(s): Empirical Inference
Bibtex Type: Conference Paper (inproceedings)

DOI: 10.1109/DEVLRN.2009.5175520
Event Name: ICDL 2009
Event Place: Shanghai, China

Address: Piscataway, NJ, USA
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

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BibTex

@inproceedings{5881,
  title = {Learning object-specific grasp affordance densities},
  author = {Detry, R. and Baseski, E. and Popovic, M. and Touati, Y. and Kr{\"u}ger, N. and Kroemer, O. and Peters, J. and Piater, J.},
  journal = {Proceedings of the 8th IEEE International Conference on Development and Learning (ICDL 2009)},
  booktitle = {8th IEEE International Conference on Development and Learning},
  pages = {1-7},
  publisher = {IEEE Service Center},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {Piscataway, NJ, USA},
  month = jun,
  year = {2009},
  month_numeric = {6}
}