Transferring human locomotion skills to humanoid robots is a multidisciplinary research field that combines biomechanics, machine learning, and neuroscience. The goal is to investigate and apply the neural mechanisms of human locomotion to develop effective locomotion controllers for humanoid robots that can replicate human-like movements and maintain balance. Despite significant progress in this area, there are still several challenges to be addressed. For instance, it is not yet clear how to map vision-captured human motion to the robot’s kinematic structure, and how to effectively apply machine learning techniques, such as reinforcement learning, to teach the robot how to walk or perform other locomotion tasks. Further research is needed to overcome these challenges and achieve reliable transfer of human locomotion skills to humanoid robots.