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arXiv · 2603.20711

RoboECC: Multi-Factor-Aware Edge-Cloud Collaborative Deployment for VLA Models

Zihao Zheng·Hangyu Cao·Jiayu Chen·Sicheng Tian·Chenyue Li·Maoliang Li·Xinhao Sun·Guojie Luo·Xiang Chen
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Abstract

Vision-Language-Action (VLA) models are mainstream in embodied intelligence but face high inference costs. Edge-Cloud Collaborative (ECC) deployment offers an effective fix by easing edge-device computing pressure to meet real-time needs. However, existing ECC frameworks are suboptimal for VLA models due to two challenges: (1) Diverse model structures hinder optimal ECC segmentation point identification; (2) Even if the optimal split point is determined, changes in network bandwidth can cause performance drift. To address these issues, we propose a novel ECC deployment framework for various VLA models, termed RoboECC. Specifically, we propose a model-hardware co-aware segmentation strategy to help find the optimal segmentation point for various VLA models. Moreover, we propose a network-aware deployment adjustment approach to adapt to the network fluctuations for maintaining optimal performance. Experiments demonstrate that RoboECC achieves a speedup of up to 3.28x with only 2.55%~2.62% overhead.

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Zihao Zheng, Hangyu Cao, Jiayu Chen, Sicheng Tian, Chenyue Li, Maoliang Li, Xinhao Sun, Guojie Luo, Xiang Chen. 2026-03-21. RoboECC: Multi-Factor-Aware Edge-Cloud Collaborative Deployment for VLA Models. https://arxiv.org/abs/2603.20711

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