LiteNWM: Efficient Latent World Models for Onboard Visual Navigation in the Wild
LiteNWM selects optimal robot paths without reconstructing images by predicting future states based on actions and history. It combines a latent predictor that forecasts future visual features across multiple time horizons with a learned scorer that compares candidate paths to the current best path. This method significantly improves navigation success in real-world, unseen environments while maintaining high efficiency.