World models aim to learn action-controlled prediction models and have proven essential for the development of intelligent agents. However, most existing world models rely heavily on substantial action-labeled data and costly training, making it challenging to adapt to novel environments with heterogeneous actions through limited interactions. This limitation can hinder their applicability across broader domains. To overcome this challenge, we propose AdaWorld, an innovative world model learning approach that enables efficient adaptation. The key idea is to incorporate action information during the pretraining of world models. This is achieved by extracting latent actions from videos in a self-supervised manner, capturing the most critical transitions between frames. We then develop an autoregressive world model that conditions on these latent actions. This learning paradigm enables highly adaptable world models, facilitating efficient transfer and learning of new actions even with limited interactions and finetuning. Our comprehensive experiments across multiple environments demonstrate that AdaWorld achieves superior performance in both simulation quality and visual planning.

本研究解决了现有世界模型在适应新环境时对大量有标签动作数据的依赖问题。通过在预训练阶段提取视频中的潜在动作，提出了一种新的自适应学习方法AdaWorld，从而实现了高效的世界模型适应能力。实验结果表明，该方法在仿真质量和视觉规划方面均表现优越，具有重要的应用潜力。

自适应世界：带有潜在动作的可适应世界模型学习