[Submitted on 26 Oct 2026]
Title:cuArms: GPU-Accelerated Conflict-Based Planning for Multi-Arm Manipulation
View a PDF of the paper titled cuArms: GPU-Accelerated Conflict-Based Planning for Multi-Arm Manipulation, by Philip Huang and 4 other authors
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Abstract:Multi-arm manipulation has the potential to dramatically improve the throughput, efficiency, and dexterity of many manipulation tasks. Recent work coordinates multiple robot arms in a shared workcell by allocating and scheduling tasks for greater parallelism, jointly planning multi-arm motions, or directly learning multi-arm policies. So far, these approaches are either too slow, rely on domain-specific assumptions, or scale poorly with the number of robots and task horizon. Thus, we introduce cuArms, a conflict-based planner co-designed with GPU parallelism that efficiently decomposes the search space using reusable multi-modal roadmaps for individual robots and resolves conflicts and deadlocks through a lazy search. To jointly optimize task assignment, schedules, motions, and manipulation skills, we integrate a high-level scheduler that proposes candidate skeletons for cuArms to ground into coordinated manipulation plans. Experimental results show that our method establishes a new state of the art, with real-time initial solutions and anytime improvements in both motion quality and scheduling, without days of domain-specific training required by learning approaches. Videos of both simulated and real-world industrial tasks are available on this https URL.
Comments: 8 pages, 8 figures
Subjects:
Robotics (cs.RO); Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)
Cite as: arXiv:2610.36250 [cs.RO]
(or arXiv:2610.36250v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2610.36250
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Philip Huang [view email] [v1] Mon, 26 Oct 2026 20:31:00 UTC (4,378 KB)
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