ComVLA: 6G接続ロボティクスにおけるVLAモデルの通信認識型分割推論
ComVLA: Communication-Aware Split Inference for VLA Models in 6G-Connected Robotics
言語指示に含まれる意味情報を利用して、VLAモデルの視覚トークン数を無線チャネル容量に適応させるフレームワークを提案し、計算量と遅延を削減しつつタスク成功率を維持した。
著者: Boliang Liu, Wint Yi Poe, Jingyun Di, Riccardo Trivisonno, Giuseppe Caire
分類: cs.RO
原文アブストラクト
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.