Effective transfer learning of affordances for household robots

Learning how to use functional objects is essential for robots that are to carry out household tasks. However, learning every object from scratch would be a very naive and time-consuming approach. In this paper, we propose transfer learning of affordances to reduce the number of exploratory actions needed to learn how to use a new object. Through embodied interaction with the object, the robot discovers the object´s similarity to previously learned objects by comparing their shape features and spatial relations between object parts. The robot actively selects object parts along with parameterized actions and evaluates the effects on-line. We demonstrate through real-world experiments with the humanoid robot NAO that our method is able to speed up the use of a new type of garbage can by transferring the affordances learned previously for similar garbage cans.