English
 
User Manual Privacy Policy Disclaimer Contact us
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
  Learning object-specific grasp affordance densities

Detry, R., Baseski, E., Popovic, M., Touati, Y., Krüger, N., Krömer, O., et al. (2009). Learning object-specific grasp affordance densities. In 2009 IEEE 8th International Conference on Development and Learning (pp. 1-7). Piscataway, NJ, USA: IEEE Service Center.

Item is

Basic

show hide
Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-C489-4 Version Permalink: http://hdl.handle.net/21.11116/0000-0002-F8F8-F
Genre: Conference Paper

Files

show Files

Locators

show
hide
Description:
-

Creators

show
hide
 Creators:
Detry, R, Author
Baseski, E, Author
Popovic, 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              

Content

show
hide
Free keywords: -
 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.

Details

show
hide
Language(s):
 Dates: 2009-06
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1109/DEVLRN.2009.5175520
BibTex Citekey: 5881
 Degree: -

Event

show
hide
Title: 8th IEEE International Conference on Development and Learning
Place of Event: Shanghai, China
Start-/End Date: 2009-06-05 - 2009-06-07

Legal Case

show

Project information

show

Source 1

show
hide
Title: 2009 IEEE 8th International Conference on Development and Learning
Source Genre: Proceedings
 Creator(s):
Affiliations:
Publ. Info: Piscataway, NJ, USA : IEEE Service Center
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 1 - 7 Identifier: ISBN: 978-1-4244-4118-1