NDLI: Learning object-specific grasp affordance densities

Content Provider	IEEE Xplore Digital Library
Author	Detry, R. Baseski, E. Popovic, M. Touati, Y. Kruger, N. Kroemer, O. Peters, J. Piater, J.
Copyright Year	2009
Description	Author affiliation: University of Southern Denmark, Denmark (Baseski, E.; Popovic, M.; Touati, Y.; Kruger, N.) \|\| MPI for Biological Cybernetics, Tübingen, Germany (Kroemer, O.; Peters, J.) \|\| University of Liège, Belgium (Detry, R.; Piater, J.)
Abstract	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.
Starting Page	1
Ending Page	7
File Size	1237543
Page Count	7
File Format	PDF
ISBN	9781424441174
DOI	10.1109/DEVLRN.2009.5175520
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-06-05
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Solid modeling Biological system modeling Humans Probability density function Density functional theory Autonomous agents Encoding Grippers Kernel Robots
Content Type	Text
Resource Type	Article

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