Humanoid Robot Locomotion

The field aims to make humanoids move safely and efficiently while maintaining balance on flat ground, stairs, slopes, gaps, and cluttered paths. Controllers range from model-based methods such as model predictive control and footstep planning to reinforcement learning, often combined with motion-capture priors, terrain perception, or mixtures of specialist skills. Researchers test these approaches in simulation and on platforms such as Unitree G1 and Digit, with tasks including stepping stones, parkour, running, and push recovery. A central challenge is building policies that generalize across terrain, sensing failures, and real-world dynamics without sacrificing stability, efficiency, or natural movement.