Cognitive scientists believe adaptable intelligent agents like humans perform reasoning through learned causal mental simulations of agents and environments. The problem of learning such simulations is called predictive world modeling. Recently, reinforcement learning (RL) agents leveraging world models have achieved SOTA performance in game environments. However, understanding how to apply the world modeling approach in complex real-world environments relevant to mobile robots remains an open question. In this paper, we present a framework for learning a probabilistic predictive world model for real-world road environments. We implement the model using a hierarchical VAE (HVAE) capable of predicting a diverse set of fully observed plausible worlds from accumulated sensor observations. While prior HVAE methods require complete states as ground truth for learning, we present a novel sequential training method to allow HVAEs to learn to predict complete states from partially observed states only. We experimentally demonstrate accurate spatial structure prediction of deterministic regions achieving 96.21 IoU, and close the gap to perfect prediction by 62% for stochastic regions using the best prediction. By extending HVAEs to cases where complete ground truth states do not exist, we facilitate continual learning of spatial prediction as a step towards realizing explainable and comprehensive predictive world models for real-world mobile robotics applications. Code is available at https://github.com/robin-karlsson0/predictive-world-models.

翻译：认知科学家认为，像人类这样的自适应智能体通过学习到的智能体与环境因果心理模拟来进行推理。学习此类模拟的问题被称为预测性世界建模。近期，利用世界模型的强化学习智能体已在游戏环境中取得最先进的性能。然而，如何将世界建模方法应用于移动机器人相关的复杂真实世界环境仍是一个开放问题。本文提出了一种学习真实道路环境概率预测性世界模型的框架。我们采用层级变分自编码器实现该模型，该模型能够根据累积的传感器观测预测一组多样化的完全观测可能世界。针对现有层级变分自编码器方法需要完整状态作为学习真值的问题，我们提出了一种新颖的序列训练方法，使层级变分自编码器能够仅从部分观测状态中学习预测完整状态。实验证明，该模型对确定性区域的空间结构预测准确率达96.21%的平均交并比，对随机区域的最佳预测将完美预测差距缩小了62%。通过将层级变分自编码器扩展到不存在完整真值状态的情况，我们促进了空间预测的持续学习，为在真实世界移动机器人应用中实现可解释且全面的预测性世界模型迈出了关键一步。代码开源地址：https://github.com/robin-karlsson0/predictive-world-models