NavDP: Learning Sim-to-Real Navigation Diffusion Policy with Privileged Information Guidance

Understand

Learning to navigate in dynamic and complex open-world environments is a critical yet challenging capability for autonomous robots.

• Existing approaches often rely on cascaded modular frameworks, which require extensive hyperparameter tuning or learning from limited real-world demonstration data.
• In this paper, we propose Navigation Diffusion Policy (NavDP), an end-to-end network trained solely in simulation that enables zero-shot sim-to-real transfer across diverse environments and robot embodiments.
• The core of NavDP is a unified transformer-based architecture that jointly learns trajectory generation and trajectory evaluation, both conditioned solely on local RGB-D observation.