A1: 完全透過型オープンソースの適応的かつ効率的なトランケート型視覚言語行動モデル
A1: A Fully Transparent Open-Source, Adaptive and Efficient Truncated Vision-Language-Action Model
低コストで高スループットな推論を実現するため、事前学習済みVLMと適応的早期終了・層間トランケートフローマッチングを導入したVLAモデルA1を提案し、シミュレーションと実機でSOTA性能と推論コスト削減を達成した。
詳しい要約
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2. 先行研究と比べてどこがすごい？
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著者: Kaidong Zhang, Jian Zhang, Rongtao Xu, Yu Sun, Shuoshuo Xue, Youpeng Wen, Xiaoyu Guo, Minghao Guo, Weijia Liufu, Liu Zihou, Kangyi Ji, Yangsong Zhang, Jiarun Zhu, Jingzhi Liu, Zihang Li, Ruiyi Chen, Meng Cao, Jingming Zhang, Shen Zhao, Xiaojun Chang, Feng Zheng, Ivan Laptev, Xiaodan Liang
分類: cs.RO
原文アブストラクト
Vision-Language-Action (VLA) models have emerged as a powerful paradigm for open-world robot manipulation, but their practical deployment is often constrained by cost: billion-scale VLM backbones and iterative diffusion/flow-based action heads incur high latency and compute, making real-time control expensive on commodity hardware. We present A1, a fully open-source and transparent VLA framework designed for low-cost, high-throughput inference without sacrificing manipulation success; Our approach leverages pretrained VLMs that provide implicit affordance priors for action generation. We release the full training stack (training code, data/data-processing pipeline, intermediate checkpoints, and evaluation scripts) to enable end-to-end reproducibility. Beyond optimizing the VLM alone, A1 targets the full inference pipeline by introducing a budget-aware adaptive inference scheme that jointly accelerates the backbone and the action head. Specifically, we monitor action consistency across intermediate VLM layers to trigger early termination, and propose Inter-Layer Truncated Flow Matching that warm-starts denoising across layers, enabling accurate actions with substantially fewer effective denoising iterations. Across simulation benchmarks (LIBERO, VLABench) and real robots (Franka, AgiBot), A1 achieves state-of-the-art success rates while significantly reducing inference cost (e.g., up to 72% lower per-episode latency for flow-matching inference and up to 76.6% backbone computation reduction with minor performance degradation). On RoboChallenge, A1 achieves an average success rate of 29.00%, outperforming baselines including pi0(28.33%), X-VLA (21.33%), and RDT-1B (15.00%).
