Title: Semantic World Models
Primary area: applications to robotics, autonomy, planning
Keywords: Robotics, World Models, Vision Language Models

Abstract: Planning with world models offers a powerful paradigm for robotic control. Conventional approaches train a model to predict future frames conditioned on current frames and actions, which can then be used for planning. However, the objective of predicting future pixels is often at odds with the actual planning objective; strong pixel reconstruction does not always correlate with good planning decisions. We posit that instead of reconstructing future frames as pixels, world models only need to predict task-relevant _semantic_ information about the future. To do this, we pose world modeling as a visual question answering problem, about semantic information in _future frames_. This perspective allows world modeling to be approached with the same tools underlying vision language models. We show how vision language models can be trained as "semantic world models" through a supervised finetuning process on image-action-text data, enabling planning for decision-making while inheriting many of the generalization and robustness properties from the pretrained vision-language models. We demonstrate how such a semantic world model can be used for policy improvement on open-ended robotics tasks, leading to significant generalization improvements over typical paradigms of reconstruction-based action-conditional world modeling.

TL;DR: Learning world models using visual question answering about the future