仅用一次演示即可快速适应新环境的视觉语言动作模型
Domain Arithmetic: One-Shot VLA Adaptation under Environmental Shifts
- 通过权重向量运算与领域信息叠加实现模型快速迁移
- 仅需单次示范即在多种环境变化下超越现有方法
- 适合需要高效部署的机器人实际应用者
视觉-语言-动作（VLA）模型在环境变化（如相机姿态调整或换用不同机器人，如从Panda到UR5e）时往往表现下降。传统适配方法需为每项任务收集多组示范，成本高昂。为此，我们提出基于类比的域自适应方法——域算术（DART），仅需一次示范即可完成模型迁移。DART通过在权重向量的奇异分量间进行子空间对齐，精准提取并添加领域特异性信息，过滤噪声成分。在模拟与真实场景中，DART均在一次性适应场景下优于现有方法，适用于多种视觉与本体变化。代码已开源：https://github.com/snumprlab/dart。
原文摘要 · Abstract (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.
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