People-aware navigation for goal-oriented behavior involving a human partner

In order to facilitate effective autonomous interaction behavior for human-robot interaction the robot should be able to execute goal-oriented behavior while reacting to sensor feedback related to the people with which it is interacting. Prior work has demonstrated that autonomously sensed distance-based features can be used to correctly detect user state. We wish to demonstrate that such models can also be used to weight action selection as well. This paper considers the problem of moving to a goal along with a partner, demonstrating that a learned model can be used to weight trajectories of a navigation system for autonomous movement. This paper presents a realization of a person-aware navigation system which requires no ad-hoc parameter tuning, and no input other than a small set of training examples. This system is validated using an in-lab demonstration of people-aware navigation using the described system.