Navigating the Future: World Models in Embodied AI

This chapter explores the complexities of world models in embodied AI, focusing on how neural networks use observational data to simulate future states and navigate dynamic environments. The discussion highlights the challenges and methodologies involved in training these models with diverse datasets while ensuring they accurately reflect physical laws. It also emphasizes the balance between creative exploration and precise navigation in unfamiliar settings, underscoring the potential for scaling data to improve navigational performance.