# MAPS-X: Explainable Multi-Robot Motion Planning via Segmentation

arXiv: https://arxiv.org/abs/2010.16106 · HTML: https://mlsanity.com/paper/2010.16106/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Oct 2020.

## Built on by (most cited first, 3 of 3)

- [Conflict-Based Search for Explainable Multi-Agent Path Finding](https://mlsanity.com/paper/2202.09930/) (2022)
- [Explaining Decentralized Multi-Agent Reinforcement Learning Policies](https://mlsanity.com/paper/2511.10409/) (2025)
- [KDTwin: Task-Aware Knowledge Distillation for Lightweight Multi-Task Driving Scene Segmentation](https://mlsanity.com/paper/2609.18955/) (2026)

## Abstract (arXiv)

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.