Abstract. Motion planning has seen much attention over the past two decades. A great deal of progress has been made in sampling-based planning, whereby a planner builds an approximate representation of the space. While these planners have demonstrated success in many scenarios, there are still difficult problems where they lack robustness or efficiency, e.g., narrow spaces. Conversely, human intuition can often determine a general solution to these problems quite effectively, but humans lack the speed and precision necessary to perform the corresponding lowlevel tasks (such as collision checking) in a timely manner. In this work, we introduce a novel strategy called Region Steering in which the user and a PRM planner work cooperatively to map the space. Region Steering utilizes two-way communication to integrate the strengths of both the user and the planner, thereby overcoming the weaknesses inherent to relying on either one alone. In one communication direction, a user can input regions, or bounding volumes in the workspace, to bias sampling towards or away from these areas. In the other direction, the planner displays its progress to the user and colors the regions based on their perceived usefulness to the system. We show that the probabilistic completeness of the underlying PRM is maintained. We demonstrate that leveraging user input enables Region Steering to provide significantly reduced mapping time and roadmap size compared with automated PRMs, e.g., Gaussian PRM, and roadmap customizability through region attraction or avoidance.

This paper summarizes the fourth Evaluating Collaborative Enterprises (ECE) workshop, held as part of the 12 th IEEE International Workshops on Enabling