Learning objects and grasp affordances through autonomous exploration

Kraft, D., Detry, R., Pugeault, N. , Başeski, E., Piater, J. and Krüger, N. (2009) Learning objects and grasp affordances through autonomous exploration. In: Fritz, M., Schiele, B. and Piater, J. H. (eds.) Computer Vision Systems: 7th International Conference on Computer Vision Systems, ICVS 2009 Liège, Belgium, October 13-15, 2009. Proceedings. Series: Lecture notes in computer science (5815). Springer: Berlin ; New York, pp. 235-244. ISBN 9783642046667 (doi: 10.1007/978-3-642-04667-4_24)

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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.

Item Type:Book Sections
Status:Published
Glasgow Author(s) Enlighten ID:Pugeault, Dr Nicolas
Authors: Kraft, D., Detry, R., Pugeault, N., Başeski, E., Piater, J., and Krüger, N.
College/School:College of Science and Engineering > School of Computing Science
Publisher:Springer
ISBN:9783642046667

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