Domain Arithmetic: One-Shot VLA Adaptation under Environmental Shifts
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 demonstrat...
| Authors | Taewook Kang, Taeheon Kim, Donghyun Shin et al. |
|---|---|
| Primary category | cs.RO |
| Published | 1 July 2026 |
| Last updated at source | 1 July 2026 |
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