VLASH: 未来状態を考慮した非同期推論によるリアルタイムVLA
VLASH: Real-Time VLAs via Future-State-Aware Asynchronous Inference
ロボットの状態を前回の行動チャンクで先読みし、予測と実行の時間ずれを解消することで、追加コストなしにVLAの非同期推論を高速化・安定化する手法を提案。
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？
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著者: Jiaming Tang, Yufei Sun, Yilong Zhao, Shang Yang, Yujun Lin, Zhuoyang Zhang, James Hou, Yao Lu, Zhijian Liu, Song Han
分類: cs.RO, cs.AI, cs.LG
原文アブストラクト
Vision-Language-Action models (VLAs) are becoming increasingly capable across diverse robotic tasks. However, these models are typically deployed under synchronous inference, where the robot waits for model inference to complete before acting, and cannot perceive or respond to environmental changes during action execution. This not only introduces noticeable action stalls, but also significantly increases reaction latency, fundamentally limiting the applicability of VLAs to dynamic, real-time tasks. Asynchronous inference offers a promising solution to achieve continuous and low-latency control by enabling robots to execute actions and perform inference simultaneously. However, because the robot and environment continue to evolve during inference, a temporal misalignment arises between the prediction and execution intervals. This leads to significant action instability, while existing asynchronous methods either degrade accuracy or introduce runtime overhead to mitigate it. We propose VLASH, a simple yet effective method for asynchronous VLA inference that delivers smooth, accurate, and fast reaction control without architectural changes or additional runtime overhead. VLASH leverages the future execution-time state by rolling the robot state forward with the previous action chunk, thereby bridging the gap between prediction and execution. Experiments show that VLASH reduces reaction latency by up to 11.8x compared to synchronous inference and consistently outperforms all asynchronous baselines in accuracy. With action quantization, it further achieves 1.5-2.0x task completion speedup with minimal accuracy loss. Moreover, it empowers state-of-the-art VLAs such as $\pi_{0.5}$ to handle fast-reaction, high-precision tasks including playing ping-pong and playing whack-a-mole, where traditional synchronous inference fails. Code is available at https://github.com/mit-han-lab/vlash
