Learning and generalization of behavior-grounded tool affordances

This paper describes an approach which a robot can use to learn the effects of its actions with a tool, as well as identify which frames of reference are useful for predicting these effects. The robot learns the tool representation during a behavioral babbling stage in which it randomly explores the space of its actions and perceives their effects. The experimental results show that the robot is able to learn a compact and accurate model of how its tool actions would affect the position of a target object. Furthermore, the model learned by the robot can generalize and perform well even with tools that the robot has never seen before. Experiments were conducted in a dynamics robot simulator. Two different learning algorithms and five different frames of reference were evaluated based on their generalization performance.