DAMP: Humanoid Locomotion via Denoised Belief Learning and Adversarial Motion Priors
DAMP is a reinforcement learning framework for humanoid walking over difficult terrain by inferring hidden, task-relevant information from sensory data using recurrent neural networks. It combines denoising world learning to reconstruct latent states with an adversarial motion prior to ensure natural, human-like movement. This approach enables robust locomotion that generalizes well from simulation to the real world.