How experts explain motion planner output: a preliminary user-study to inform the design of explainable planners

Abstract

Motion planning is a hard problem that can often overwhelm both users and designers: due to the difficulty in understanding the optimality of a solution, or reasons for a planner to fail to find any solution. Inspired by recent work in machine learning and task planning, in this paper we are guided by a vision of developing motion planners that can provide reasons for their output—thus potentially contributing to better user interfaces, debugging tools, and algorithm trustworthiness. Towards this end, we propose a preliminary taxonomy and a set of important considerations for the design of explainable motion planners, based on the analysis of a comprehensive user study of motion planning experts. We identify the kinds of things that need to be explained by motion planners ("explanation objects"), types of explanation, and several procedures required to arrive at explanations. We also elaborate on a set of qualifications and design considerations that should be taken into account when designing explainable methods. These insights contribute to bringing the vision of explainable motion planners closer to reality, and can serve as a resource for researchers and developers interested in designing such technology.