Learning Objects and Grasp Affordances through Autonomous Exploration

Dirk Kraft, Renaud Detry, Nicolas Pugeault, Emre Baseski, Justus Piater, Norbert Krüger

Publikation: Kapitel i bog/rapport/konference-proceedingKonferencebidrag i proceedingsForskningpeer review

Abstract

We describe a system for autonomous learning of visual object representations and their grasp affordances on a robot-vision system. It segments objects by grasping and moving 3D scene features, and creates probabilistic visual representations for object detection, recognition and pose estimation, which are then augmented by continuous characterizations of grasp affordances generated through biased, random exploration. Thus, based on a careful balance of generic prior knowledge encoded in (1) the embodiment of the system, (2) a vision system extracting structurally rich information from stereo image sequences as well as (3) a number of built-in behavioral modules on the one hand, and autonomous exploration on the other hand, the system is able to generate object and grasping knowledge through interaction with its environment.
OriginalsprogEngelsk
TitelComputer Vision Systems : 7th International Conference on Computer Vision Systems, ICVS 2009 Liège, Belgium, October 13-15, 2009. Proceedings
ForlagSpringer
Publikationsdato2009
Sider235-244
ISBN (Trykt)978-3-642-04666-7
DOI
StatusUdgivet - 2009
BegivenhedInternational Conference on Computer Vision Systems, 2009 - Liège, Belgien
Varighed: 13. okt. 200915. okt. 2009
Konferencens nummer: 7

Konference

KonferenceInternational Conference on Computer Vision Systems, 2009
Nummer7
Land/OmrådeBelgien
ByLiège
Periode13/10/200915/10/2009
NavnLecture Notes in Computer Science
Vol/bind5815/2009
ISSN0302-9743

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