Action Chunk Scheduling for Batched Robot Policy Serving
arXiv:2608.00337v1 Announce Type: new Abstract: Deploying robot foundation models at scale is the next step towards realizing the potential of general-purpose robots. However, Vision-Language-Action (VLA) and other foundation models are computationally demanding, and on-device compute is constrained by power and space. In this paper, we introduce the problem of serving a robot policy to multiple robots from a remote GPU and formulate it as a scheduling problem. We build Armory, a serving system validated on fleets of both simulated and real robots. Our experiments show that naive scheduling heuristics perform well when all robots are the same, but fall short when robots consume action chunks at different rates, uncovering a mismatch between conventional batching methods and the closed-loop requirements of robot policy execution. To address this, we propose a scheduling algorithm that accounts for this heterogeneity and improves overall system throughput by up to $18\%$ in real-world experiments. Additional details are available at https://gatech-rl2.github.io/actionchunkscheduling.
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[Submitted on 31 Jul 2026]
Title:Action Chunk Scheduling for Batched Robot Policy Serving
View a PDF of the paper titled Action Chunk Scheduling for Batched Robot Policy Serving, by Rohan Bansal and 6 other authors
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Abstract:Deploying robot foundation models at scale is the next step towards realizing the potential of general-purpose robots. However, Vision-Language-Action (VLA) and other foundation models are computationally demanding, and on-device compute is constrained by power and space. In this paper, we introduce the problem of serving a robot policy to multiple robots from a remote GPU and formulate it as a scheduling problem. We build Armory, a serving system validated on fleets of both simulated and real robots. Our experiments show that naive scheduling heuristics perform well when all robots are the same, but fall short when robots consume action chunks at different rates, uncovering a mismatch between conventional batching methods and the closed-loop requirements of robot policy execution. To address this, we propose a scheduling algorithm that accounts for this heterogeneity and improves overall system throughput by up to $18\%$ in real-world experiments. Additional details are available at this https URL.
Subjects:
Robotics (cs.RO)
Cite as: arXiv:2608.00337 [cs.RO]
(or arXiv:2608.00337v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2608.00337
arXiv-issued DOI via DataCite (pending registration)
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From: Rohan Bansal [view email] [v1] Fri, 31 Jul 2026 22:58:59 UTC (1,429 KB)
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