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APEX-MR: Multi-Robot Asynchronous Planning and Execution for Cooperative Assembly

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arxiv 2503.15836 v3 pith:F2MKDVGG submitted 2025-03-20 cs.RO

classification cs.RO
keywords apex-mrassemblylegosystemexecutionmulti-robotplanningtasks
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Compared to a single-robot workstation, a multi-robot system offers several advantages: 1) it expands the system's workspace, 2) improves task efficiency, and, more importantly, 3) enables robots to achieve significantly more complex and dexterous tasks, such as cooperative assembly. However, coordinating the tasks and motions of multiple robots is challenging due to issues, e.g., system uncertainty, task efficiency, algorithm scalability, and safety concerns. To address these challenges, this paper studies multi-robot coordination and proposes APEX-MR, an asynchronous planning and execution framework designed to safely and efficiently coordinate multiple robots to achieve cooperative assembly, e.g., LEGO assembly. In particular, APEX-MR provides a systematic approach to post-process multi-robot tasks and motion plans to enable robust asynchronous execution under uncertainty. Experimental results demonstrate that APEX-MR can significantly speed up the execution time of many long-horizon LEGO assembly tasks by 48% compared to sequential planning and 36% compared to synchronous planning on average. To further demonstrate performance, we deploy APEX-MR in a dual-arm system to perform physical LEGO assembly. To our knowledge, this is the first robotic system capable of performing customized LEGO assembly using commercial LEGO bricks. The experimental results demonstrate that the dual-arm system, with APEX-MR, can safely coordinate robot motions, efficiently collaborate, and construct complex LEGO structures. Our project website is available at https://intelligent-control-lab.github.io/APEX-MR/.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. ScheduleStream: Temporal Planning with Samplers for GPU-Accelerated Multi-Arm Task and Motion Planning & Scheduling

    cs.RO 2025-11 conditional novelty 6.0 of 10

    ScheduleStream extends sampling-based task and motion planning with durative actions and temporal scheduling so a bimanual robot can plan and execute parallel arm motions, roughly halving makespan versus sequential planning.

  2. NeSyPack: A Neuro-Symbolic Framework for Bimanual Logistics Packing

    cs.RO 2025-06 conditional novelty 5.0 of 10

    NeSyPack, a hierarchical neuro-symbolic controller, achieved high packing success rates and won the WBCD competition at ICRA 2025.

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