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Learning relational affordance models for robots in multi-object manipulation tasks

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5 Author(s)
Moldovan, B. ; Dept. of Comput. Sci., Katholieke Univ. Leuven, Leuven, Belgium ; Moreno, P. ; van Otterlo, M. ; Santos-Victor, J.
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Affordances define the action possibilities on an object in the environment and in robotics they play a role in basic cognitive capabilities. Previous works have focused on affordance models for just one object even though in many scenarios they are defined by configurations of multiple objects that interact with each other. We employ recent advances in statistical relational learning to learn affordance models in such cases. Our models generalize over objects and can deal effectively with uncertainty. Two-object interaction models are learned from robotic interaction with the objects in the world and employed in situations with arbitrary numbers of objects. We illustrate these ideas with experimental results of an action recognition task where a robot manipulates objects on a shelf.

Published in:

Robotics and Automation (ICRA), 2012 IEEE International Conference on

Date of Conference:

14-18 May 2012