Learning Unsupervised World Models for Autonomous Driving via Discrete Diffusion

Learning world models can teach an agent how the world works in an unsupervised manner. Even though it can be viewed as a special case of sequence modeling, progress for scaling world models on robotic applications such as autonomous driving has been somewhat less rapid than scaling language models with Generative Pre-trained Transformers (GPT). We identify two reasons as major bottlenecks: dealing with complex and unstructured observation space, and having a scalable generative model. Consequently, we propose a novel world modeling approach that first tokenizes sensor observations with VQVAE, then predicts the future via discrete diffusion. When applied to learning world models on point cloud observations, our model reduces prior SOTA Chamfer distance by more than 65% for 1s prediction, and more than 50% for 3s prediction, across NuScenes, KITTI Odometry, and Argoverse2 datasets. Our results demonstrate that discrete diffusion on tokenized agent experience can unlock the power of GPT-like unsupervised learning for robotic agents. Figure 1: Our unsupervised world model can produce accurate near-term 1s predictions and diverse multi-future 3s predictions directly on the level of point cloud observations. World models explicitly represent the knowledge of an autonomous agent about its environment. They are defined as a generative model that predicts the next observation in an environment given past observations and the current action. Such a generative model can learn from any unlabeled agent experience, and can be used for both learning and planning in the model-based reinforcement learning framework (Sutton, 1991). This approach has excelled in domains such as Atari (Kaiser et al., 2019), robotic manipulation (Nagabandi et al., 2020), and Minecraft (Hafner et al., 2023). Learning world models can be viewed as a special case of sequence modeling on agent experience. While Generative Pre-trained Transformers (GPT) (Brown et al., 2020) have enabled rapid progress Prediction systems in autonomous driving still require supervised learning, either on the level of bounding boxes (Luo et al., 2018), semantic segmentation (Sadat et al., 2020), or instance segmentation (Hu et al., 2021). However, just as GPT learns to understand language via next token prediction, if a world model can predict unlabeled future observations really well, it must have developed a general understanding of the scene including geometry and dynamics.