AsyncVLA: An Asynchronous VLA for Fast and Robust Navigation on the Edge

• URL: http://arxiv.org/abs/2602.13476v1
• Date: Fri, 13 Feb 2026 21:31:19 GMT
• Title: AsyncVLA: An Asynchronous VLA for Fast and Robust Navigation on the Edge
• Authors: Noriaki Hirose, Catherine Glossop, Dhruv Shah, Sergey Levine,
• Abstract summary: High latency breaks the control loop, rendering powerful models unsafe for real-time deployment.<n>We propose AsyncVLA, an asynchronous control framework that decouples semantic reasoning from reactive execution.<n>AsyncVLA achieves a 40% higher success rate than state-of-the-art baselines.
• Score: 49.66156306240961
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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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