G-MAPP: GPU-accelerated Multi-Agent Planning and Perception for Reactive Motion Generation
This paper presents G-MAPP, a GPU-accelerated framework for reactive motion generation that achieves up to 5x speedup by parallelizing world modeling and planning on GPU, enabling real-time perception-action coupling in dynamic environments.
[2606.12579] G-MAPP: GPU-accelerated Multi-Agent Planning and Perception for Reactive Motion Generation
[Submitted on 10 Jun 2026]
Title:G-MAPP: GPU-accelerated Multi-Agent Planning and Perception for Reactive Motion Generation
View a PDF of the paper titled G-MAPP: GPU-accelerated Multi-Agent Planning and Perception for Reactive Motion Generation, by Tanmay Bishnoi and Riddhiman Laha and Tobias L\"ow and Jose Alex Chandy and Luis F. C. Figueredo and Sami Haddadin
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Abstract:Reactive motion generation in unstructured environments remains an open challenge in robotics. Due to the computational complexity of collision-free motion generation, existing methods either generate global trajectories for static scenarios, or employ models that make conservative assumptions about the environment. This paper identifies the primary bottleneck as the runtime performance demand of planning on high-fidelity environments, and the temporal integration between the perception and planning modules. Therefore, we propose a framework that does not compromise on runtime performance and world representations for perception and planning by accelerating world modeling and vector-field based planning using the GPU. This allows us to achieve faster parallel state exploration for quasi-global trajectory planning, and tighter coupling of the perception-action loop in real-time for dynamic cluttered environments with off-the-shelf depth sensors. We quantitatively evaluate the computation-time and success rate differences for the CPU and GPU versions of our planner, and perform qualitative evaluations of our coupled framework using real-world experiments on a 7-DoF Franka Emika robot. Experimental results demonstrate that our GPU-based framework achieves up to a 5x speedup over the CPU version and successfully avoids collisions across both trivial and challenging physical world scenarios.
Comments: The implementation is available at: this https URL
Subjects:
Robotics (cs.RO)
Cite as: arXiv:2606.12579 [cs.RO]
(or arXiv:2606.12579v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2606.12579
arXiv-issued DOI via DataCite (pending registration)
Journal reference: IEEE Robotics and Automation Letters, vol. 11, no. 6, pp. 7516-7523, June 2026
Related DOI:
https://doi.org/10.1109/LRA.2026.3678839
DOI(s) linking to related resources
Submission history
From: Tanmay Bishnoi [view email] [v1] Wed, 10 Jun 2026 18:28:24 UTC (7,521 KB)
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