Asymmetric Interpretations of Positive and Negative Human Feedback for a Social Learning Agent

Abstract

The ability for people to interact with robots and teach them new skills will be crucial to the successful application of robots in everyday human environments. In order to design agents that learn efficiently and effectively from their instruction, it is important to understand how people, that are not experts in Machine Learning or robotics, will try to teach social robots. In prior work we have shown that human trainers use positive and negative feedback differentially when interacting with a reinforcement learning agent. In this paper we present experiments and implementations on two platforms, a robotic and a computer game platform, that explore the asymmetric communicative intents of positive and negative feedback from a human partner, in particular that negative feedback is both about the past and about intentions for future action.