  Knowledge from animals and humans inspires robotic innovations. Numerous
efforts have been made to achieve agile locomotion in quadrupedal robots
through classical controllers or reinforcement learning approaches. These
methods usually rely on physical models or handcrafted rewards to accurately
describe the specific system, rather than on a generalized understanding like
animals do. Here we propose a hierarchical framework to construct primitive-,
environmental- and strategic-level knowledge that are all pre-trainable,
reusable and enrichable for legged robots. The primitive module summarizes
knowledge from animal motion data, where, inspired by large pre-trained models
in language and image understanding, we introduce deep generative models to
produce motor control signals stimulating legged robots to act like real
animals. Then, we shape various traversing capabilities at a higher level to
align with the environment by reusing the primitive module. Finally, a
strategic module is trained focusing on complex downstream tasks by reusing the
knowledge from previous levels. We apply the trained hierarchical controllers
to the MAX robot, a quadrupedal robot developed in-house, to mimic animals,
traverse complex obstacles and play in a designed challenging multi-agent chase
tag game, where lifelike agility and strategy emerge in the robots.
