给机器人换环境，一个演示就够了
机器人学会一个任务后，换个摄像头角度或换台长得像的机器人，往往就失灵了。以前得重新录几十次演示才能适应，现在研究者发明了一种叫DART的方法，只需要一次演示，通过调整模型内部的“权重向量”来提取新环境的关键特征，就能让机器人立刻在新场景里干活。在模拟和真实实验中，它比现有方法都强。虽然你明天用不上，但它让机器人从实验室走向真实世界的成本大幅降低。
📄 原文摘要(英文)
Vision-Language-Action (VLA) models often fail to perform the same learned tasks under environmental shifts, such as changes in camera pose and shifts to a different but similar robot (e.g., from Panda to UR5e). Adapting these models to the shifted environment (i.e., target domain) often requires training on multiple demonstrations for each task, which are costly to collect. To reduce the burden of data curation and training, we propose an analogy-based method that adapts VLA models under environmental shifts through weight vector arithmetic with domain-specific information addition, named Domain ARiThmetic (DART). Unlike prior approaches, DART requires collecting only a single demonstration, enabling efficient adaptation. To accurately isolate domain-specific information for addition, DART performs subspace alignment between singular components in weight vectors to filter out noisy components. In both simulated and real-world experiments, DART outperforms existing VLA adaptation methods in one-shot scenarios across diverse visual and embodiment shifts. Code is available at https://github.com/snumprlab/dart.