Ep#15 Navigation World Models

The podcast explores Navigation World Models with Amir Bar, a computer vision and deep learning expert. Bar explains world models as simulations of environments based on sensory input like images, processed through deep neural networks. The discussion highlights the utility of world models in simulating future outcomes to inform action choices, and in building complex simulators. A key challenge addressed is ensuring the model considers action relevance, solved by predicting randomly selected future states rather than just the immediate next step. The podcast also examines the architecture of the model, which uses diffusion transformers, and the action embedding method, which allows for training on datasets lacking full trajectory data. The conversation concludes with a look toward future research directions, particularly in manipulation tasks.