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Discovering affordances through perception and manipulation | IEEE Conference Publication | IEEE Xplore

Discovering affordances through perception and manipulation


Abstract:

Considering perception as an observation process only is the very reason for which robotic perception methods are to date unable to provide a general capacity of scene un...Show More

Abstract:

Considering perception as an observation process only is the very reason for which robotic perception methods are to date unable to provide a general capacity of scene understanding. Related work in neuroscience has shown that there is a strong relationship between perception and action. We believe that considering perception in relation to action requires to interpret the scene in terms of the agent's own potential capabilities. In this paper, we propose a Bayesian approach for learning sensorimotor representations through the interaction between action and observation capabilities. We represent the notion of affordance as a probabilistic relation between three elements: objects, actions and effects. Experiments for affordances discovery were performed on a real robotic platform in an unsupervised way assuming a limited set of innate capabilities. Results show dependency relations that connect the three elements in a common frame: affordances. The increasing number of interactions and observations results in a Bayesian network that captures the relationships between them. The learned representation can be used for prediction tasks.
Date of Conference: 09-14 October 2016
Date Added to IEEE Xplore: 01 December 2016
ISBN Information:
Electronic ISSN: 2153-0866
Conference Location: Daejeon, Korea (South)

References

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