  Traditional reinforcement learning control for quadruped robots often relies
on privileged information, demanding meticulous selection and precise
estimation, thereby imposing constraints on the development process. This work
proposes a Self-learning Latent Representation (SLR) method, which achieves
high-performance control policy learning without the need for privileged
information. To enhance the credibility of our proposed method's evaluation,
SLR is compared with open-source code repositories of state-of-the-art
algorithms, retaining the original authors' configuration parameters. Across
four repositories, SLR consistently outperforms the reference results.
Ultimately, the trained policy and encoder empower the quadruped robot to
navigate steps, climb stairs, ascend rocks, and traverse various challenging
terrains. Robot experiment videos are at https://11chens.github.io/SLR/
