RoboECC: Multi-Factor-Aware Edge-Cloud Collaborative Deployment for VLA Models
Fuente:
arXiv
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| Auteurs principaux: | , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866908994811985920 |
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| author | Zheng, Zihao Cao, Hangyu Chen, Jiayu Tian, Sicheng Li, Chenyue Li, Maoliang Sun, Xinhao Luo, Guojie Chen, Xiang |
| author_facet | Zheng, Zihao Cao, Hangyu Chen, Jiayu Tian, Sicheng Li, Chenyue Li, Maoliang Sun, Xinhao Luo, Guojie Chen, Xiang |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_20711 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | RoboECC: Multi-Factor-Aware Edge-Cloud Collaborative Deployment for VLA Models Zheng, Zihao Cao, Hangyu Chen, Jiayu Tian, Sicheng Li, Chenyue Li, Maoliang Sun, Xinhao Luo, Guojie Chen, Xiang Distributed, Parallel, and Cluster Computing Machine Learning Robotics 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. |
| title | RoboECC: Multi-Factor-Aware Edge-Cloud Collaborative Deployment for VLA Models |
| topic | Distributed, Parallel, and Cluster Computing Machine Learning Robotics |
| url | https://arxiv.org/abs/2603.20711 |