  Not until recently, robust bipedal locomotion has been achieved through
reinforcement learning. However, existing implementations rely heavily on
insights and efforts from human experts, which is costly for the iterative
design of robot systems. Also, styles of the learned motion are strictly
limited to that of the reference. In this paper, we propose a new way to learn
bipedal locomotion from a simple sine wave as the reference for foot heights.
With the naive human insight that the two feet should be lifted up
alternatively and periodically, we experimentally demonstrate on the Cassie
robot that, a simple reward function is able to make the robot learn to walk
end-to-end and efficiently without any explicit knowledge of the model. With
custom sine waves, the learned gait pattern can also have customized styles.
Codes are released at github.com/WooQi57/sin-cassie-rl.
