  We are interested in the design of autonomous robot behaviors that learn the
preferences of users over continued interactions, with the goal of efficiently
executing navigation behaviors in a way that the user expects. In this paper,
we discuss our work in progress to modify a general model for robot navigation
behaviors in an exploration task on a per-user basis using preference-based
reinforcement learning. The novel contribution of this approach is that it
combines reinforcement learning, motion planning, and natural language
processing to allow an autonomous agent to learn from sustained dialogue with a
human teammate as opposed to one-off instructions.
