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arXiv · 2601.11250

VLAgents: A Policy Server for Efficient VLA Inference

Tobias Jülg·Khaled Gamal·Nisarga Nilavadi·Pierre Krack·Seongjin Bien·Michael Krawez·Florian Walter·Wolfram Burgard
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Abstract

The rapid emergence of Vision-Language-Action models (VLAs) has a significant impact on robotics. However, their deployment remains complex due to the fragmented interfaces and the inherent communication latency in distributed setups. To address this, we introduce VLAgents, a modular policy server that abstracts VLA inferencing behind a unified Gymnasium-style protocol. Crucially, its communication layer transparently adapts to the context by supporting both zero-copy shared memory for high-speed simulation and compressed streaming for remote hardware. In this work, we present the architecture of VLAgents and validate it by integrating seven policies -- including OpenVLA and Pi Zero. In a benchmark with both local and remote communication, we further demonstrate how it outperforms the default policy servers provided by OpenVLA, OpenPi, and LeRobot. VLAgents is available at https://github.com/RobotControlStack/vlagents

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Tobias Jülg, Khaled Gamal, Nisarga Nilavadi, Pierre Krack, Seongjin Bien, Michael Krawez, Florian Walter, Wolfram Burgard. 2026-01-16. VLAgents: A Policy Server for Efficient VLA Inference. https://arxiv.org/abs/2601.11250

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