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，像人类这样具有适应能力的智能体通过基于因果关系的心理模拟（即对智能体及环境的习得性因果心理模拟）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在游戏环境中取得了最先进的性能。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性问题。本文提出了一种针对真实道路环境学习概率性预测世界模型的框架。我们采用分层变分自编码器实现该模型，能够根据累积的传感器观测预测一组多样化的完整可观测世界。由于现有分层变分自编码器方法需要完整状态作为学习所需的真实标注，我们提出了一种新颖的序贯训练方法，使分层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。实验表明，该模型在确定性区域的空间结构预测中达到了96.21的交并比，并在随机区域的最佳预测中将与完美预测的差距缩小了62%。通过将分层变分自编码器扩展到不存在完整真实标注状态的情况，我们促进了空间预测的持续学习，向着实现真实世界移动机器人应用中的可解释且全面的预测性世界模型迈出了重要一步。代码地址：https://github.com/robin-karlsson0/predictive-world-models。