ComVLA: Communication-Aware Split Inference for VLA Models in 6G-Connected Robotics

2026-09-07

AI Summary

为解决6G连接机器人中VLA模型推理计算量大及无线传输限制问题，提出ComVLA框架，通过语言指导调整视觉令牌数量以适应信道容量，减少计算和延迟。

Abstract

Connected robotics is an emerging 6G application where mobile robots follow natural-language instructions to manipulate physical objects. The Vision-Language-Action (VLA) models that enable this are too large to run on the robot; a common trend is to offload inference to the cloud. The wireless link, however, limits how much sensing data the edge can transmit per control step. Two recent lines address this constraint: semantic communication codecs compress sensor data but require channel-specific retraining, and VLA token pruners select tokens from image but ignore the channel. Our insight is that the dense semantic information contained in the language already indicates which visual tokens matter. We propose ComVLA, a framework that uses this language guidance to adapt the VLA token budget to the channel capacity. Transmitting 32 tokens instead of 512 on the LIBERO benchmark, ComVLA cuts inference compute by 74% and inference latency by 22% versus the original OpenVLA-OFT baseline, at a cost of 1.5 pp in average task success (95.4% vs. 96.9%), and it stays within the capacity budget under Rayleigh and Rician fading. These results demonstrate that co-designing VLA inference and wireless communication is a practical direction for 6G-connected robotics.

Problem

• VLA Models
• 6G-Connected Robotics
• Wireless Communication
• Inference Offloading
• Data Transmission

Innovation

• Communication-Aware
• Split Inference
• VLA Models
• Token Budget Adaptation
• Channel Capacity