Robotic foundation models achieve strong generalization by leveraging internet-scale vision-language representations, but their massive computational cost creates a fundamental bottleneck: high inference latency. In dynamic environments, this latency breaks the control loop, rendering powerful models unsafe for real-time deployment. We propose AsyncVLA, an asynchronous control framework that decouples semantic reasoning from reactive execution. Inspired by hierarchical control, AsyncVLA runs a large foundation model on a remote workstation to provide high-level guidance, while a lightweight, onboard Edge Adapter continuously refines actions at high frequency. To bridge the domain gap between these asynchronous streams, we introduce an end-to-end finetuning protocol and a trajectory re-weighting strategy that prioritizes dynamic interactions. We evaluate our approach on real-world vision-based navigation tasks with communication delays up to 6 seconds. AsyncVLA achieves a 40% higher success rate than state-of-the-art baselines, effectively bridging the gap between the semantic intelligence of large models and the reactivity required for edge robotics.

翻译：机器人基础模型通过利用互联网规模的视觉-语言表征实现了强大的泛化能力，但其巨大的计算成本带来了一个根本性瓶颈：高推理延迟。在动态环境中，这种延迟会破坏控制环路，使得强大模型无法安全地实时部署。我们提出了AsyncVLA，一种将语义推理与反应式执行解耦的异步控制框架。受分层控制启发，AsyncVLA在远程工作站上运行大型基础模型以提供高层指导，同时一个轻量级的板载边缘适配器以高频率持续优化动作。为弥合这些异步流之间的领域差距，我们引入了一种端到端的微调协议和一种优先考虑动态交互的轨迹重加权策略。我们在具有高达6秒通信延迟的真实世界视觉导航任务上评估了我们的方法。AsyncVLA相比最先进的基线实现了40%更高的成功率，有效弥合了大型模型的语义智能与边缘机器人所需的反应能力之间的差距。