Learning to press doorbell buttons

This paper describes an approach that a robot can use to learn to press doorbell buttons. This approach combines exploratory behaviors with an active learning strategy to enable the robot to learn faster how and where it should press a button in order to trigger the buzzer. The framework was tested with an upper-torso humanoid robot on seven different doorbell buttons. Three different active learning exploration strategies were evaluated: random, stimulus-driven, and uncertainty-driven. The results show that an active learning strategy can significantly speedup the robot's learning progress. Among the three strategies that were evaluated, the uncertainty-driven strategy was the most effective.