The recent mainstream 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 the proposed method's evaluation, SLR was directly compared with state-of-the-art algorithms using their open-source code repositories and original configuration parameters. Remarkably, SLR surpasses the performance of previous methods using only limited proprioceptive data, demonstrating significant potential for future applications. Ultimately, the trained policy and encoder empower the quadruped robot to traverse various challenging terrains. Videos of our results can be found on our website: https://11chens.github.io/SLR/
Risk-Aware Reinforcement Learning with Bandit-Based Adaptation for Quadrupedal Locomotion
Department of Electrical and Computer Engineering, University of California Los Angeles（加州大学洛杉矶分校电气与计算机工程系）；Department of Mechanical and Aerospace Engineering, University of California Los Angeles（加州大学洛杉矶分校机械与航空航天工程系）
SoloParkour: Constrained Reinforcement Learning for Visual Locomotion from Privileged Experience
LAAS-CNRS（法国国家科学研究中心自动化与系统分析实验室）；Université de Toulouse（图卢兹大学）；CNRS（法国国家科学研究中心）；New York University（纽约大学）；Artificial and Natural Intelligence Toulouse Institute（图卢兹人工与自然智能研究所）
SKooP: Symmetric Koopman Predictions for Faster and More Generalizable Legged Robot Locomotion with Reinforcement Learning
Italian Institute of Technology (IIT)（意大利理工学院）；University of Manchester（曼彻斯特大学）；Dynamic Legged Systems Laboratory, IIT（意大利理工学院动态腿式系统实验室）；Generative Bionics S.R.L（生成仿生学有限公司）；DAUIN, Politecnico di Torino（都灵理工大学DAUIN）；Rehab Technologies Lab, IIT（意大利理工学院康复技术实验室）；Newcastle University（纽卡斯尔大学）
