WorldVLA: Towards Autoregressive Action World Model
This work addresses the challenge of improving action and image prediction in AI systems, though it appears incremental by building on existing VLA and world model frameworks.
The authors tackled the problem of unifying action and image understanding and generation by proposing WorldVLA, an autoregressive action world model that integrates Vision-Language-Action and world models, demonstrating that it outperforms standalone models and improves action chunk generation with an attention mask strategy.
We present WorldVLA, an autoregressive action world model that unifies action and image understanding and generation. Our WorldVLA intergrates Vision-Language-Action (VLA) model and world model in one single framework. The world model predicts future images by leveraging both action and image understanding, with the purpose of learning the underlying physics of the environment to improve action generation. Meanwhile, the action model generates the subsequent actions based on image observations, aiding in visual understanding and in turn helps visual generation of the world model. We demonstrate that WorldVLA outperforms standalone action and world models, highlighting the mutual enhancement between the world model and the action model. In addition, we find that the performance of the action model deteriorates when generating sequences of actions in an autoregressive manner. This phenomenon can be attributed to the model's limited generalization capability for action prediction, leading to the propagation of errors from earlier actions to subsequent ones. To address this issue, we propose an attention mask strategy that selectively masks prior actions during the generation of the current action, which shows significant performance improvement in the action chunk generation task.