ENCP enables VLN agents to reliably quantify uncertainty across multi-step navigation sequences, allowing them to know when to ask for help—a practical safety feature for embodied AI systems.
This paper addresses uncertainty estimation for vision-language navigation (VLN) agents—systems that follow natural language instructions while navigating visual environments. The authors propose ENCP, a method that adapts conformal prediction (a statistical framework for uncertainty quantification) to handle the sequential, variable-length nature of navigation episodes.