Simulated Humanoid

Simulated humanoid research focuses on developing realistic and controllable digital representations of humans for various applications, primarily aiming to improve locomotion, manipulation, and social interaction capabilities. Current research leverages reinforcement learning, often combined with techniques like adversarial imitation learning and model predictive control, to train controllers that enable human-like gaits, object grasping, and navigation in complex environments, sometimes incorporating motion capture data for improved training efficiency. This work is significant for advancing robotics, animation, and human-computer interaction by providing robust and efficient methods for simulating complex human behaviors and interactions.