PRIOR: Perceptive Learning for Humanoid Locomotion with Reference Gait Priors

PRIOR is a framework that teaches humanoid robots to walk naturally across complex terrains like stairs and gaps by combining three key components: a motion capture-based gait generator for realistic walking patterns, a neural network that interprets depth camera images to understand terrain, and smart reward signals that guide foot placement. The system achieves 100% success on various terrain challenges without needing adversarial training or extensive real-world calibration, and the researchers plan to release it as an open-source tool for future humanoid robotics research.