Towards a cognitive robot that uses internal rehearsal to learn affordance relations

This paper introduces a new approach to develop robots that can learn general affordance relations from their experiences. Our approach is a part of larger efforts to develop a cognitive robot and has two components: (a) the robot models affordances as statistical relations among actions, object properties and the effects of actions on objects, in the context of a goal that specifies preferred effects and outcomes, (b) to exploit the general-knowledge potential of actual experiences, the robot engages in internal rehearsal by playing out virtual scenarios grounded in yet different from actual experiences. To the extent the robot accurately appreciates affordance relations, the robot can autonomously predict the outcomes of its behaviors before executing them. Internal rehearsal-based outcome production in turn facilitates planning of a sequence of behaviors toward successful task execution. We also report simulation results of internal rehearsal-based traversability affordance learning of a humanoid robot.