Action Chunk Scheduling for Batched Robot Policy Serving

2026-07-31

AI Summary

This work addresses the challenge of serving policy inference for multiple heterogeneous robots from a remote GPU, where conventional batched scheduling fails to accommodate disparities in action chunk consumption rates, thereby limiting system throughput. The authors formulate this scenario as a scheduling problem and introduce Armory, a system featuring the first batched scheduling algorithm that explicitly accounts for heterogeneity in action chunk consumption—departing from traditional homogeneous assumptions to better align with the realities of robotic closed-loop control. Armory integrates remote GPU batched inference, deployment of Vision-Language-Action models, and coordination mechanisms for heterogeneous robots. Evaluations on both real-world and simulated robot clusters demonstrate its efficacy, achieving up to an 18% improvement in overall throughput compared to naive scheduling strategies.

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.

Problem

• robot policy serving
• action chunk scheduling
• batched inference
• heterogeneous robots
• foundation models

Innovation

• Action Chunk Scheduling
• Robot Policy Serving
• Heterogeneous Robot Fleets
• Batched Inference
• Foundation Model Deployment