  Learning to navigate unknown environments from scratch is a challenging
problem. This work presents a system that integrates world models with
curiosity-driven exploration for autonomous navigation in new environments. We
evaluate performance through simulations and real-world experiments of varying
scales and complexities. In simulated environments, the approach rapidly and
comprehensively explores the surroundings. Real-world scenarios introduce
additional challenges. Despite demonstrating promise in a small controlled
environment, we acknowledge that larger and dynamic environments can pose
challenges for the current system. Our analysis emphasizes the significance of
developing adaptable and robust world models that can handle environmental
changes to prevent repetitive exploration of the same areas.
