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ICVS
2009
Springer

Learning Objects and Grasp Affordances through Autonomous Exploration

9 years 5 months ago
Learning Objects and Grasp Affordances through Autonomous Exploration
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.
Dirk Kraft, Renaud Detry, Nicolas Pugeault, Emre B
Added 26 May 2010
Updated 26 May 2010
Type Conference
Year 2009
Where ICVS
Authors Dirk Kraft, Renaud Detry, Nicolas Pugeault, Emre Baseski, Justus H. Piater, Norbert Krüger
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