Abstract Reasoning jointly on perception and action requires to interpret the scene in terms
of the agent's own potential capabilities. We propose a Bayesian architecture for learning …

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 …

Endowing artificial agents with the ability of predicting the consequences of their own
actions and efficiently planning their behaviors based on such predictions is a fundamental …

Recently, the aspect of visual perception has been explored in the context of Gibson's
concept of affordances [1] in various ways [4-9]. In extension to existing functional views on …

This work is about the relevance of Gibson's concept of affordances for visual perception in
interactive and autonomous robotic systems. In extension to existing functional views on …

Recently, the aspect of visual perception has been explored in the context of Gibson's
concept of affordances [1] in various ways. We focus in this work on the importance of …

The perception of affordances provides an action-centered parametric representation of the
environment. By perceiving an object's visual features in terms of what actions they afford …

A novel behavior representation is introduced that permits a robot to systematically explore
the best methods by which to successfully execute an affordance-based behavior for a …

This work is about the relevance of Gibson's concept of affordances [1] for visual perception
in interactive and autonomous robotic systems. In extension to existing functional views on …

The concept of object affordances describes the possible ways whereby an agent (either
biological or artificial) can act upon an object. By observing the effects of actions on objects …