  Parkour is a grand challenge for legged locomotion, even for quadruped
robots, requiring active perception and various maneuvers to overcome multiple
challenging obstacles. Existing methods for humanoid locomotion either optimize
a trajectory for a single parkour track or train a reinforcement learning
policy only to walk with a significant amount of motion references. In this
work, we propose a framework for learning an end-to-end vision-based
whole-body-control parkour policy for humanoid robots that overcomes multiple
parkour skills without any motion prior. Using the parkour policy, the humanoid
robot can jump on a 0.42m platform, leap over hurdles, 0.8m gaps, and much
more. It can also run at 1.8m/s in the wild and walk robustly on different
terrains. We test our policy in indoor and outdoor environments to demonstrate
that it can autonomously select parkour skills while following the rotation
command of the joystick. We override the arm actions and show that this
framework can easily transfer to humanoid mobile manipulation tasks. Videos can
be found at https://humanoid4parkour.github.io
