Using Human Reinforcement Learning Models to Improve Robots as Teachers

Abstract

Robotic teaching has not received nearly as much research attention as robotic learning. In this research, we used the humanoid robot Baxter to provide feedback and positive reinforcement to human participants attempting to achieve a complex task. Our robot autonomously casts the teaching problem as one that invokes the exploration/exploitation tradeoff to understand the cognitive strategy of its human partner and develop an effective motivational approach. We compare our learned reinforcement model with a baseline non-reinforcement approach and with a random reinforcer.