Article thumbnail

Modeling affordances using bayesian networks

By Luis Montesano, Manuel Lopes, Alexandre Bernardino and Jose Santos-victor

Abstract

Abstract — 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. I

Year: 2007
OAI identifier: oai:CiteSeerX.psu:10.1.1.495.8482
Provided by: CiteSeerX
Download PDF:
Sorry, we are unable to provide the full text but you may find it at the following location(s):
  • http://welcome.isr.ist.utl.pt/... (external link)
  • http://welcome.isr.ist.utl.pt/... (external link)
  • http://citeseerx.ist.psu.edu/v... (external link)
  • Suggested articles


    To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.