Object recognition has traditionally been approached using primarily vision-based strategies. Recent research suggests, however, that intelligent agents use more than vision in order to comprehend and classify their environment. In this work we investigate an agent's ability to recognize objects on the basis of nonvisual proprioceptive information generated by its body. An experiment is presented in which an industrial robot collects and structures information about various objects in terms of its physical configuration. This information is then analyzed using a Bayesian model, which is used subsequently for classifying objects.
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