SLR: Learning Quadruped Locomotion without Privileged Information
<|reference_start|>SLR: Learning Quadruped Locomotion without Privileged Information: 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/<|reference_end|>
@article{chen2024slr:,
    title={SLR: Learning Quadruped Locomotion without Privileged Information},
    author={Shiyi Chen, Zeyu Wan, Shiyang Yan, Chun Zhang, Weiyi Zhang, Qiang Li,
  Debing Zhang, Fasih Ud Din Farrukh},
    journal={arXiv preprint arXiv:2406.04835},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.04835},
    primaryClass={cs.RO}
}