Learning Joint Policies for Human-Robot Dialog and Co-Navigation

Service robots need language capabilities for communicating with people, and navigation skills for beyond-proximity interaction in the real world. When the robot explores the real world with people side by side, there is the compound problem of human-robot dialog and co-navigation. The human-robot team uses dialog to decide where to go, and their shared spatial awareness affects the dialog state. In this paper, we develop a framework that learns a joint policy for humanrobot dialog and co-navigation toward efficiently and accurately completing tour guide and information delivery tasks. We show that our approach outperforms baselines from the literature in task completion rate and execution time, and demonstrate our approach in the real world.