ロボット実行制約下でVLAモデルを効率的に配信するシステム
Scaling VLA Serving Under Robotic Execution Constraints
多数のロボットにVLAモデルを配信する際の推論コストを削減するため、計算の並列化と干渉管理を両立させたサービングシステムXInferを提案し、実運用ワークロードで有効ロボット容量を1.26〜3.75倍に向上させた。
詳しい要約
1. どんなもの？
2. 先行研究と比べてどこがすごい？
3. 技術・手法の肝は？
4. どうやって有効だと検証した？
5. 議論はある？
6. 次に読むべき論文は？
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著者: Yue Zhang, Zicong Ye, Lufang Chen, Dance Yang, Mingyang Li, Zhenwei Duan, Michael Zhao, Ziye Deng, Starrick Liu, Ruizhi Chengze, YongHua Chen, SongAo Han, Yu Sun, Ran Yu, Xiaofan Li, Ruyi Gan, Hao Wang
分類: cs.RO, cs.DC
原文アブストラクト
Vision-language-action (VLA) models have emerged as a promising paradigm for general-purpose robotic manipulation. In our production setting, hundreds of VLA-powered robots are already operating in real-world environments, creating an immediate need for efficient fleet serving. We present XInfer, a production-deployed VLA serving system designed to reduce the inference infrastructure cost of large-scale robotic deployments. XInfer jointly coordinates heterogeneous VLA computation with the evolving response requirements of robotic execution. By exploiting computational parallelism while managing interference, XInfer improves GPU efficiency while protecting time-critical robotic feedback. We evaluate XInfer across three VLA models using production-derived workloads, achieving 1.26-3.75 times the effective robot capacity (ERC) of state-of-the-art systems under physical-execution constraints. Our implementation is publicly available at https://github.com/X-Square-Robot/xinfer.