Automating Behavior Selection for Affective Telepresence Robot

The tabletop robot Haru, used for affective telepresence research, enables a teleoperator to communicate affects from a distance. The robot’s expressiveness offers myriad ways of communicating affects through the execution of emotive routines. The teleoperator reacts to input modalities such as the user’s facial expression, gestures and speech-based intent as perceived by the robot’s perception system. However, due to the sheer number of routines to select from, the task of choosing the appropriate or the most preferred routine is becoming cumbersome. In this paper, we propose a human-in-the-loop reinforcement learning mechanism in which an agent learns the teleoperator’s selection preference as a function of the input modalities and aids the routine selection process by narrowing it to n-best optimal choices. Our experimental results show that with only a few number of interactions from the teleoperator, the system can learn to recommend optimal routine behaviors for all perceived modalities, which greatly reduces the workload of the teleoperator.