Title: Learning relational affordance models for robots in multi-object manipulation tasks
Authors: Moldovan, Bogdan
Moreno, Plinio
van Otterlo, Martijn
Santos-Victor, Jose
De Raedt, Luc #
Issue Date: 17-May-2012
Host Document: IEEE International Conference on Robotics and Automation, ICRA 2012 pages:4373 -4378
Conference: ICRA (IEEE International Conference on Robotics and Automation) edition:29 location:St Paul, Minnesota, USA date:14-18 May 2012
Abstract: 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.
ISBN: 978-1-4673-1403-9
Publication status: published
KU Leuven publication type: IC
Appears in Collections:Informatics Section
# (joint) last author

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