Toward Learning the Binding Affordances of Objects : A Behavior-Grounded Approach

This paper introduces a developmental approach to learning the binding affordances of objects by a robot. A behavior-based framework is used to ground the affordance representation in the behavioral repertoire of the robot. The affordances are learned during a behavioral babbling stage in which the robot randomly chooses sequences of exploratory behaviors, applies them to the objects, and detects invariants in the resulting set of observations. The invariants are calculated relative to the robot’s body. The approach was implemented and tested in a dynamics robot simulator.