Domain Arithmetic: One-Shot VLA Adaptation under Environmental Shifts

Vision-Language-Action (VLA) models often fail to performthe same learned tasks under environmental shifts, such as changes incamera pose and shifts to a different but similar robot (e.g., from Pandato UR5e). Adapting these models to the shifted environment (i.e., targetdomain) often requires training on multiple demonstrations for each task,which are costly to collect. To reduce the burden of data curation andtraining, we propose an analogy-based method that adapts VLA modelsunder environmental shifts through weight vector arithmetic with domain-specific information addition, named Domain ARiThmetic (DART). Un-like prior approaches, DART requires collecting only a single demonstra-tion, enabling efficient adaptation. To accurately isolate domain-specificinformation for addition, DART performs subspace alignment between sin-gular components in weight vectors to filter out noisy components. In bothsimulated and real-world experiments, DART outperforms existing VLAadaptation methods in one-shot scenarios across diverse visual and embod-iment shifts. Code is available at https://github.com/snumprlab/dart.