让大模型远程决策，边缘设备快速执行，提升机器人导航的实时性与成功率。
AsyncVLA: An Asynchronous VLA for Fast and Robust Navigation on the Edge
- 大模型远程做语义判断，边缘小模型高频执行动作，实现异步协同。
- 在6秒延迟下，成功率达92%，比现有方法高40%。
- 适合需要强智能与高响应的边缘机器人应用。
机器人基础模型通过互联网规模的视觉-语言表征实现强大泛化能力，但其巨大的计算开销导致推理延迟过高，成为主要瓶颈：在动态环境中，延迟会破坏控制回路，使强大模型无法安全用于实时部署。我们提出AsyncVLA，一种异步控制框架，将语义推理与反应式执行解耦。受分层控制启发，大型基础模型在远程工作站运行以提供高层指导，而轻量级的机载边缘适配器则以高频持续优化动作。为弥合这两条异步流之间的领域差距，我们引入端到端微调协议和轨迹重加权策略，优先关注动态交互。我们在真实世界的视觉导航任务上评估该方法，通信延迟最高达6秒。AsyncVLA相比最先进基线实现40%更高的成功率，有效连接了大模型的语义智能与边缘机器人所需的反应能力。
原文摘要 · Abstract (English)
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.
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