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2007
IEEE

Modeling affordances using Bayesian networks

12 years 2 months ago
Modeling affordances using Bayesian networks
— Affordances represent the behavior of objects in terms of the robot’s motor and perceptual skills. This type of knowledge plays a crucial role in developmental robotic systems, since it is at the core of many higher level skills such as imitation. In this paper, we propose a general affordance model based on Bayesian networks linking actions, object features and action effects. The network is learnt by the robot through interaction with the surrounding objects. The resulting probabilistic model is able to deal with uncertainty, redundancy and irrelevant information. We evaluate the approach using a real humanoid robot that interacts with objects.
Luis Montesano, Manuel Lopes, Alexandre Bernardino
Added 03 Jun 2010
Updated 03 Jun 2010
Type Conference
Year 2007
Where IROS
Authors Luis Montesano, Manuel Lopes, Alexandre Bernardino, José Santos-Victor
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