Training a robot with evaluative feedback and unlabeled guidance signals

In this paper, we present a new method for training a robot by natural interaction using evaluative feedback and unlabeled guidance signals. Feedback signals are directly mapped to reward values and used for learning both the task and the meaning of the guidance signals. The learned guidance signals are used in return to bootstrap task learning. We propose to use unlabeled guidance signals as an alternative solution to preprogrammed guidance. We evaluate our method both in simulation and on a real robot.