教机器人看穿动作背后的物理变化
现在的机器人学动作，大多只是把「看到什么」直接映射成「做什么」，像背答案，换个场景就露馅。这篇 VLA-Δ 换了个思路：让模型在学动作的同时，必须学会预测「从这一刻到下一刻，物体发生了什么物理变化」——比如杯子被拿起、门被推开。研究者为此造了一个大规模数据集，给每个动作都标上这种物理状态变化，作为额外的学习目标。训练时模型要同时答对动作和变化，但用的时候只看当前画面就能直接出动作，不增加任何计算负担。它在标准测试和真实机器人上都追平或超过了现有最强模型。这不是你明天能装进家里的东西，但它指向一个更扎实的方向：让机器人不是模仿动作，而是理解动作背后的因果。
📄 原文摘要(英文)
Vision-language-action (VLA) models transfer semantic knowledge acquired through vision-language pretraining to robot control, achieving strong performance across various benchmarks. However, most VLAs directly map observations to demonstrated actions without explicit supervision of intermediate physical state changes, making them prone to overfitting observation-action correspondences. Some methods introduce intermediate reasoning or future-state prediction in language, visual, or latent spaces to guide action generation, but do not explicitly supervise the physical state transition itself. We therefore propose VLA-Δ, a general framework that uses physical changes between current and future states as explicit learning targets. To support this objective, we construct a large-scale pretraining dataset and derive physical state-transition annotations covering task-relevant semantics and visual motion changes, providing more direct supervision for continuous action generation. During inference, the model generates actions directly from current observations without additional inference overhead. VLA-Δ achieves performance competitive with the state of the art on LIBERO and RoboTwin, and outperforms π_{0.5} in real-robot experiments. Our code is available at https://github.com/ZJU-OmniAI/VLA-Delta