Users' belief awareness in reinforcement learning-based situated human-robot dialogue management

Abstract

Others can have a different perception of the world than ours. Understanding this divergence is an ability, known as perspective taking in developmental psychology, that humans exploit in daily social interactions. A recent trend in robotics aims at endowing robots with similar mental mechanisms. The goal then is to enable them to naturally and efficiently plan tasks and communicate about them. In this paper we address this challenge extending a state-of-the-art goal-oriented dialogue management framework, the Hidden Information State (HIS). The new version makes use of the robot's awareness of the users' belief in a reinforcement learning-based situated dialogue management optimisation procedure. Thus the proposed solution enables the system to cope not only with the communication ambiguities due to noisy channel but also with the possible misunderstandings due to some divergence among the beliefs of the robot and its interlocutor in a human-robot interaction (HRI) context. We show the relevance of the approach by comparing different handcrafted and learnt dialogue policies with and without divergent belief reasoning in an in-house pick-place-carry scenario by means of user trials in a simulated 3D environment.