Humanoid Locomotion

Humanoid locomotion research aims to enable robots to walk and navigate diverse terrains with human-like agility and robustness. Current efforts heavily utilize reinforcement learning, often coupled with transformer models or other neural network architectures, to learn complex control policies from simulated and real-world data, sometimes incorporating curriculum learning for efficient training. This field is crucial for advancing robotics, with implications for applications ranging from search and rescue to assistive technologies and manufacturing, as robust and adaptable locomotion is a fundamental prerequisite for many real-world tasks.