4 research outputs found
Learning to Navigate from Scratch using World Models and Curiosity: the Good, the Bad, and the Ugly
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.Comment: IROS 2023 workshop World Models and Predictive Coding in Cognitive
Robotics and IROS 2023 workshop Learning Robot Super Autonom
Simultaneous Path Planning and Topological Mapping (SP2ATM) for environment exploration and goal oriented navigation
10.1016/j.robot.2010.12.003Robotics and Autonomous Systems593-4228-242RASO