Multi-agent path finding (MAPF) is an essential component of many large-scale, real-world
robot deployments, from aerial swarms to warehouse automation. However, despite the …

Multi-agent path finding (MAPF) is an indispensable component of large-scale robot
deployments in numerous domains ranging from airport management to warehouse …

Multi-agent pathfinding (MAPF) has been widely used to solve large-scale real-world
problems, eg, automation warehouses. The learning-based, fully decentralized framework …

Multirobot path planning leads multiple robots from start positions to designated goal
positions by generating efficient and collision-free paths. Multirobot systems realize …

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 …

Multi-agent navigation in dynamic environments is of great industrial value when deploying
a large scale fleet of robot to real-world applications. This paper proposes a decentralized …

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) …

Multi-agent path finding (MAPF) is a well-studied problem in artificial intelligence that can be
solved quickly in practice when using simplified agent assumptions. However, real-world …

Path planning for mobile robots in large dynamic environments is a challenging problem, as
the robots are required to efficiently reach their given goals while simultaneously avoiding …

Robotic navigation in environments shared with other robots or humans remains
challenging because the intentions of the surrounding agents are not directly observable …