VLAgents: VLA推論を効率化するポリシーサーバ
VLAgents: A Policy Server for Efficient VLA Inference
VLAモデルの推論を統一プロトコルで抽象化し、共有メモリと圧縮ストリーミングで通信を最適化するモジュラーポリシーサーバを提案。
詳しい要約
1. どんなもの？
2. 先行研究と比べてどこがすごい？
3. 技術・手法の肝は？
4. どうやって有効だと検証した？
5. 議論はある？
6. 次に読むべき論文は？
※ AIが要旨から生成した要約です。正確性は原文をご確認ください。
著者: Tobias Jülg, Khaled Gamal, Nisarga Nilavadi, Pierre Krack, Seongjin Bien, Michael Krawez, Florian Walter, Wolfram Burgard
分類: cs.RO
原文アブストラクト
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
