領域算術：環境變化下的一次性VLA自適應

摘要

視覺-語言-動作（VLA）模型在面對環境變化時，常無法順利執行已學習的任務，例如攝影機姿態改變，或轉換至不同但相似的機器人（例如從 Panda 到 UR5e）。要使這些模型適應變化後的環境（即目標領域），通常需要針對每個任務收集多個示範資料進行訓練，而這些資料的蒐集成本相當高昂。為減輕資料整理與訓練的負擔，我們提出一種基於類比的方法，透過帶有領域特定資訊的權重向量運算來調整 VLA 模型以適應環境變化，稱之為領域算術（Domain ARiThmetic, DART）。與先前方法不同，DART 僅需收集單一示範即可實現高效適應。為準確分離出欲加入的領域特定資訊，DART 在權重向量中的奇異分量之間進行子空間對齊，以濾除雜訊分量。在模擬與真實世界的實驗中，DART 在多樣的視覺與本體變化情境下，其單次示範的適應表現皆優於現有的 VLA 適應方法。程式碼已公開於 https://github.com/snumprlab/dart。

English

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.