Latency-Tolerant Cloud-Edge Collaborative Vision-Language-Action Models via Emergent Representational Specialization

The paper introduces CloudEdgeVLA, a cloud‑edge policy for Vision‑Language‑Action models that treats temporal misalignment as a representation‑learning problem. It encodes delayed observations into slowly varying task features on the cloud while a lightweight edge head fuses the latest cloud feature with current local vision. Experiments on four LIBERO suites show that CloudEdgeVLA retains 63.8–78.0% success under a 40‑step delay window, far outperforming VLASH and single‑path baselines.

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• robotics
• efficiency
• multimodal