  Recently, humanoid robots have made significant advances in their ability to
perform challenging tasks due to the deployment of Reinforcement Learning (RL),
however, the inherent complexity of humanoid robots, including the difficulty
of designing complicated reward functions and training entire sophisticated
systems, still poses a notable challenge. To conquer these challenges, after
many iterations and in-depth investigations, we have meticulously developed a
full-size humanoid robot, "Adam", whose innovative structural design greatly
improves the efficiency and effectiveness of the imitation learning process. In
addition, we have developed a novel imitation learning framework based on an
adversarial motion prior, which applies not only to Adam but also to humanoid
robots in general. Using the framework, Adam can exhibit unprecedented
human-like characteristics in locomotion tasks. Our experimental results
demonstrate that the proposed framework enables Adam to achieve
human-comparable performance in complex locomotion tasks, marking the first
time that human locomotion data has been used for imitation learning in a
full-size humanoid robot.
