8 papers
Lifelong LaCAM with Local Guidance for Lifelong MAPF
Tomoki Arita, Keisuke Okumura
Local guidance has recently proven to be a powerful driver of empirical performance in real-time, suboptimal multi-agent pathfinding (MAPF), improving the scalable configuration-ba…
Alternating Target-Path Planning for Scalable Multi-Agent Coordination
Yu Kumagai, Keisuke Okumura
The concurrent target assignment and pathfinding (TAPF) problem extends multi-agent pathfinding (MAPF) by asking planners to allocate distinct targets and collision-free paths to a…
Pairwise is Not Enough: Hypergraph Neural Networks for Multi-Agent Pathfinding
Rishabh Jain, Keisuke Okumura, Michael Amir +2
Multi-Agent Path Finding (MAPF) is a representative multi-agent coordination problem, where multiple agents are required to navigate to their respective goals without collisions. S…
db-LaCAM: Fast and Scalable Multi-Robot Kinodynamic Motion Planning with Discontinuity-Bounded Search and Lightweight MAPF
Akmaral Moldagalieva, Keisuke Okumura, Amanda Prorok +1
State-of-the-art multi-robot kinodynamic motion planners struggle to handle more than a few robots due to high computational burden, which limits their scalability and results in s…
Graph Attention-Guided Search for Dense Multi-Agent Pathfinding
Rishabh Jain, Keisuke Okumura, Michael Amir +1
Finding near-optimal solutions for dense multi-agent pathfinding (MAPF) problems in real-time remains challenging even for state-of-the-art planners. To this end, we develop a hybr…
ReCoDe: Reinforcement Learning-based Dynamic Constraint Design for Multi-Agent Coordination
Michael Amir, Guang Yang, Zhan Gao +3
Constraint-based optimization is a cornerstone of robotics, enabling the design of controllers that reliably encode task and safety requirements such as collision avoidance or form…