Learning Grounded Communicative Intent from Human-Robot Dialog

Abstract

Studying how a robot can learn to communicate with a person provides insight into how communication might be learned in general. Deep models of dialog and communicative intent typically rely on modeling the internal state of the speakers—states that are unobservable by a learning robot. This paper considers how communication can be framed to be learnable from experience. In particular, we describe how an agent might learn to communicate by building on three foundational capabilities, namely 1) an observable signal of satisfied intent (a smile), 2) the ability to imitate perceived actions, and 3) perceptual referents for discourse items. Early simulation results show that an agent can learn some basic communication skills from these foundations.