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.
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