10 papers
GAMBIT: Learning to Plan Continuous Multi-Robot Trajectories
Rishabh Jain, Akmaral Moldagalieva, Lorenzo Magnino +5
GAMBIT is an opening chess move in which a player sacrifices a piece, typically a pawn, to gain a positional advantage later in the game. Analogously, in multi-robot coordination,…
Denoising Multirobot Trajectories
Yuhao Zhang, Keisuke Okumura, Ajay Shankar +1
Multi-robot trajectory planning is a fundamental problem in multi-robot coordination but remains computationally challenging due to its nonconvex, multimodal, and high-dimensional…
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…
LF: Online Multi-Robot Path Planning Meets Optimal Trajectory Control
Ajay Shankar, Keisuke Okumura, Amanda Prorok
We propose a multi-robot control paradigm to solve point-to-point navigation tasks for a team of holonomic robots with access to the full environment information. The framework inv…
D4orm: Multi-Robot Trajectories with Dynamics-aware Diffusion Denoised Deformations
Yuhao Zhang, Keisuke Okumura, Heedo Woo +2
This work presents an optimization method for generating kinodynamically feasible and collision-free multi-robot trajectories that exploits an incremental denoising scheme in diffu…