Preprint Open access
RIWANav: Recursive World-Action Models with Self-Improvement for Urban Navigation
Long-horizon urban navigation requires sequential local decisions whose errors can compound over time. Imitation learning (IL) rarely learns from failures, while physical trial-and-error reinforcement learning (RL) is costly. Action-conditioned world models can provide imagined feedback by predicting visual consequence …