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/

翻译：传统四足机器人强化学习控制常依赖特权信息，需精细筛选与精确估计，限制了开发进程。本文提出自学习隐表征（SLR）方法，无需特权信息即可实现高性能控制策略学习。为增强评估可信度，将SLR与多个开源先进算法代码库进行对比，并保留原作者配置参数。在四个代码库中，SLR均持续优于参考结果。最终，训练所得策略与编码器使四足机器人能跨越台阶、攀爬楼梯、攀登岩石及穿越各类复杂地形。机器人实验视频见https://11chens.github.io/SLR/