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.
@article{arxiv.2603.20711,
title = {RoboECC: Multi-Factor-Aware Edge-Cloud Collaborative Deployment for VLA Models},
author = {Zihao Zheng and Hangyu Cao and Jiayu Chen and Sicheng Tian and Chenyue Li and Maoliang Li and Xinhao Sun and Guojie Luo and Xiang Chen},
journal= {arXiv preprint arXiv:2603.20711},
year = {2026}
}