MAPS-X: Explainable Multi-Robot Motion Planning via Segmentation
This addresses the need for explainable planning in safety-critical multi-robot systems, though it is incremental as it builds on existing planners.
The paper tackles the problem of making multi-robot motion plans verifiable by humans in safety-critical applications, proposing MAPS-X to generate plans with short visual explanations as sequences of images, and shows it achieves good performance with up to 30% reduction in explanation length compared to baseline methods.
Traditional multi-robot motion planning (MMP) focuses on computing trajectories for multiple robots acting in an environment, such that the robots do not collide when the trajectories are taken simultaneously. In safety-critical applications, a human supervisor may want to verify that the plan is indeed collision-free. In this work, we propose a notion of explanation for a plan of MMP, based on visualization of the plan as a short sequence of images representing time segments, where in each time segment the trajectories of the agents are disjoint, clearly illustrating the safety of the plan. We show that standard notions of optimality (e.g., makespan) may create conflict with short explanations. Thus, we propose meta-algorithms, namely multi-agent plan segmenting-X (MAPS-X) and its lazy variant, that can be plugged on existing centralized sampling-based tree planners X to produce plans with good explanations using a desirable number of images. We demonstrate the efficacy of this explanation-planning scheme and extensively evaluate the performance of MAPS-X.