Robotic World Model: Learning to Simulate for Robust Robot Control

Presents a neural network-based world model for model-based reinforcement learning in robotics, focusing on sim-to-real transfer for quadrupedal and humanoid robots. Enables robust policy optimization through learned environment simulation.