Simultaneous Acquisition of Task and Feedback Models

Abstract : We present a system to learn task representations from ambiguous feedback. We consider an inverse reinforcement learner that receives feedback from a teacher with an unknown and noisy protocol. The system needs to estimate simultaneously what the task is (i.e. how to find a compact representation to the task goal), and how the teacher is providing the feedback. We further explore the problem of ambiguous protocols by considering that the words used by the teacher have an unknown relation with the action and meaning expected by the robot. This allows the system to start with a set of known signs and learn the meaning of new ones. We present computational results that show that it is possible to learn the task under a noisy and ambiguous feedback. Using an active learning approach, the system is able to reduce the length of the training period.