By learning from past navigation failures and predicting outcomes before acting, zero-shot object navigation can improve significantly without retraining—achieving 10% better success rates with fewer wasted steps.
This paper presents EvolveNav, a framework for embodied agents to find objects in unseen environments without prior training. It builds a memory of successful navigation rules from past attempts and uses them to guide future exploration, reducing trial-and-error through predictive planning before taking actions.