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arXiv · 2602.10114

MR-STORM: Scalable Multi-Arm Control By Distributed MPC

Dan Evron·Elias Goldsztejn·Dan R. Suissa·Ronen I. Brafman
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Abstract

Centralized motion planning algorithms struggle to scale in multi-arm manipulation, where inter-arm coupling and tight workspaces amplify complexity. We introduce MR-STORM, a distributed sampling-based MPC framework that leverages massively parallel GPU sampling for arm control. Our approach integrates inter-arm collision avoidance via plan-sharing and a dynamic priority scheme to maintain task efficiency. We show empirically that MR-STORM outperforms existing baselines in complex simulation environments and that it transfers effectively to physical hardware. Code, demos and data are available at: https://roboworkshop.github.io/multi-robot-mpc/

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Dan Evron, Elias Goldsztejn, Dan R. Suissa, Ronen I. Brafman. 2026-09-04. MR-STORM: Scalable Multi-Arm Control By Distributed MPC. https://arxiv.org/abs/2602.10114

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