Latent World Models with Monotone Planning Costs for Image-Goal Navigation
This work proposes a latent world model built on a frozen DINO-family encoder and trains it with two complementary objectives, achieving state-of-the-art image-goal navigation performance while reducing orientation error by $2.7\times over the same-encoder DINO WM baseline.