In the Lifelong Multi-Agent Path Finding (L-MAPF) problem, agents must repeatedly move from one destination to another while avoiding obstacles and inter-agent collisions. Widely regarded as one of the highest-performing solutions to this problem is the Rolling-Horizon Collision Resolution (RHCR) framework. However, co...
Alex DeWeese, Jiao-Yang Li, Guannan Qu· 0 citations
This work proposes a search-based framework that is theoretically complete for grounding policy-generated multi-hand trajectories onto physical multi-arm systems, and explicitly searches over both the discrete assignment of trajectories to arms and the continuous Jacobian null spaces of redundant manipulators.
Yorai Shaoul, Jiao-Yang Li, Maxim Likhachev· Proceedings of the Internati...· 0 citations
This work proposes Path Updates over Staggered Horizons (PUSH), a LMAPF planner capable of coordinating thousands of agents in under a second while planning over multi-step horizons, and integrates EPIBT-inspired priority inheritance, backtracking, and anytime improvements into its windowed planning.
This work introduces a unified RL formulation that jointly optimizes agent and environment policies, where the environment policy learns graph edge costs to provide global movement guidance via backward Dijkstra search and achieves significant improvements over the strong search-based planner, Causal-PIBT, across multi...
He Jiang, Jingtian Yan, Yulun Zhang et al.· 0 citations
This work introduces Model-Based Diffusion Optimal Control (MDOC), a model-based diffusion planner that efficiently produces dynamically feasible trajectories without relying on data, and shows that MDOC's safety mechanism naturally scales to multi-robot planning settings through Conflict-Based Search.
