必要なときに反応せよ：VLAポリシーのためのイベント駆動型非同期推論
React When You Need To: Event-Triggered Asynchronous Inference for VLA Policies
VLAモデルの推論間隔をシーンの変化に応じて動的に調整するイベント駆動型非同期推論を提案し、動作の一貫性と即応性を両立させて実世界タスクで高い成功率を達成した。
著者: Yansong Wu, Huaqing Li, Tianding Hou, Lingyun Chen, Alois Knoll
分類: cs.RO
原文アブストラクト
Vision-Language-Action (VLA) models commonly predict action chunks, limiting their ability to react to environmental changes during execution. Existing asynchronous inference methods improve reactivity but typically rely on a fixed inference gap. In this paper, we propose an event-guided dynamic inference strategy that adapts the inference gap according to scene changes observed since the previous inference. Thereby, it simultaneously preserves motion consistency and prompt reactivity. Across static and dynamic real-world settings, our method consistently performs best, averaging 95% success and exceeding the strongest baseline by 55 percentage points. The code will be made publicly available upon acceptance. The project page is available at https://react-when-you-need-to.github.io/.