Recent years have shown a large increase in applications and research of problems that
include moving a fleet of physical robots. One particular application that is currently a multi …

Abstract Purpose of Review Planning collision-free paths for multiple robots is important for
real-world multi-robot systems and has been studied as an optimization problem on graphs …

Abstract Multi-Agent Path Finding (MAPF) is a challenging combinatorial problem that asks
us to plan collision-free paths for a team of cooperative agents. In this work, we show that …

Multi-Agent Path Finding (MAPF) is essential to large-scale robotic systems. Recent
methods have applied reinforcement learning (RL) to learn decentralized polices in partially …

Abstract In the Multi-Agent Path Finding (MAPF) problem, a set of agents moving on a graph
must reach their own respective destinations without inter-agent collisions. In practical MAPF …

Learning communication via deep reinforcement learning (RL) or imitation learning (IL) has
recently been shown to be an effective way to solve Multi-Agent Path Finding (MAPF) …

We consider two new classes of pairwise path symmetries which appear in the context of
Multi-Agent Path Finding (MAPF). The first of them, corridor symmetry, arises when two …

Abstract Multi-Agent Path Finding (MAPF) is the problem of finding a set of collision-free
paths for a team of agents in a common environment. MAPF is NP-hard to solve optimally …

Multi-Agent Path Finding (MAPF) is a crucial component for many large-scale robotic
systems, where agents must plan their collision-free paths to their given goal positions …

We describe a new way of reasoning about symmetric collisions for Multi-Agent Path Finding
(MAPF) on 4-neighbor grids. We also introduce a symmetry-breaking constraint to resolve …