Zengmao Wang

Predicting Consequences and Reinforcing Navigation Policies with Latent World Models

This work proposes a compatibility prediction Latent World Model for robot navigation that predicts action-conditioned latent feature compatibility rather than reconstructing observations and demonstrates how the learned world model can supervise policy learning from unlabeled video data and improve policies through reinforcement learning entirely within the world model.