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VLAgents: A Policy Server for Efficient VLA Inference

2026-01-16 · Tobias Jülg, Khaled Gamal, Nisarga Nilavadi, Pierre Krack, Seongjin Bien, Michael Krawez, Florian Walter, Wolfram Burgard arxiv

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

📄 PDF Abstract BibTeX arXiv:2601.11250

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Model-based Policy Search for Partially Measurable Systems

2021-01-21 · Fabio Amadio, Alberto Dalla Libera, Ruggero Carli, Daniel Nikovski 외

In this paper, we propose a Model-Based Reinforcement Learning (MBRL) algorithm for Partially Measurable Systems (PMS), i.e., systems where the state can not be directly measured, but must be estimated through proper sta…

Gaussian Processes model Model-based Reinforcement Learning reinforcement-learning+1

Safe Multi-Agent Deep Reinforcement Learning for Privacy-Aware Edge-Device Collaborative DNN Inference

2026-02-23 · Hong Wang, Xuwei Fan, Zhipeng Cheng, Yachao Yuan 외 arxiv

As Deep Neural Network (DNN) inference becomes increasingly prevalent on edge and mobile platforms, critical challenges emerge in privacy protection, resource constraints, and dynamic model deployment. This paper propose…

Reinforcement Learning

Multi-user Co-inference with Batch Processing Capable Edge Server

2022-06-03 · Wenqi Shi, Sheng Zhou, Zhisheng Niu, Miao Jiang 외

Graphics processing units (GPUs) can improve deep neural network inference throughput via batch processing, where multiple tasks are concurrently processed. We focus on novel scenarios that the energy-constrained mobile …

GPU Scheduling

Tiny, On-Device Decision Makers with the MiniConv Library

2025-12-17 · Carlos Purves arxiv

Reinforcement learning (RL) has achieved strong results, but deploying visual policies on resource-constrained edge devices remains challenging due to computational cost and communication latency. Many deployments theref…

Reinforcement Learning

Efficient Reinforcement Learning for Routing Jobs in Heterogeneous Queueing Systems

2024-02-02 · Neharika Jali, Guannan Qu, Weina Wang, Gauri Joshi

We consider the problem of efficiently routing jobs that arrive into a central queue to a system of heterogeneous servers. Unlike homogeneous systems, a threshold policy, that routes jobs to the slow server(s) when the q…

reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)