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About this paper
Zihao Zheng, Hangyu Cao, Jiayu Chen, Sicheng Tian, Chenyue Li, Maoliang Li, Xinhao Sun, Guojie Luo and 1 more
RoboECC is an Edge-Cloud Collaborative (ECC) framework designed to achieve real-time inference for Vision-Language-Action (VLA) models on resource-constrained edge devices. It enables efficient deployment by intelligently segmenting VLA models based on their diverse structures and dynamically adjusting the deployment strategy in response to fluctuating network conditions, resulting in up to 3.28x speedups and smoother robotic control in real-world applications.
Key insights
- Optimal segmentation for VLA models necessitates awareness of their heterogeneous internal structures, as different components (e.g., LLM vs. Diffusion) exhibit distinct computational and bandwidth demands.
- ECC frameworks for VLA models must dynamically adapt to real-time network bandwidth fluctuations due to the models' sensitivity to network latency, stemming from multi-step inference and short output sequences.
- Enabling rapid, dynamic segmentation adjustments requires pre-storing parameters of 'candidate' split-point layers on both edge and cloud devices to avoid prohibitive latency from on-the-fly parameter transfers.
Summary by alphaXiv.
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