DART: One-Shot Cross-Environment Adaptation for VLA Robots
_akhaliq · x · 2026-07-03
A research team from Seoul National University proposed DART, a method using weight-space arithmetic to enable one-shot adaptation of Vision-Language-Action (VLA) models under environmental changes. This approach allows robot policies to generalize quickly to new scenarios without large-scale retraining, significantly lowering the deployment barrier for embodied AI. The paper has been released on the HuggingFace Papers platform.
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