标题： MULE：多地形和未知负载适应以实现有效的四足运动 显示英文标题

标题： MULE: Multi-terrain and Unknown Load Adaptation for Effective Quadrupedal Locomotion

摘要： 四足机器人越来越多地被用于各种地形的负重任务。虽然基于模型预测控制（MPC）的方法可以考虑负载变化，但它们通常依赖于预定义的步态计划或轨迹生成器，限制了它们在非结构化环境中的适应性。为了解决这些局限性，我们提出了一种自适应强化学习（RL）框架，使四足机器人能够动态适应不同的负载和多样的地形。该框架包括一个负责基本运动的基准策略和一个学习校正行为以在负载变化下保持稳定并提高命令跟踪的自适应策略。我们通过在Isaac Gym中的大规模仿真实验以及在Unitree Go1四足机器人上的真实硬件部署验证了所提出的方案。控制器在平坦地面、斜坡和楼梯上，在静态和动态负载变化下进行了测试。在所有设置中，我们的自适应控制器在跟踪身体高度和速度命令方面始终优于现有控制器，展示了增强的鲁棒性和适应性，而无需显式步态设计或手动调整。 显示英文摘要

摘要： Quadrupedal robots are increasingly deployed for load-carrying tasks across diverse terrains. While Model Predictive Control (MPC)-based methods can account for payload variations, they often depend on predefined gait schedules or trajectory generators, limiting their adaptability in unstructured environments. To address these limitations, we propose an Adaptive Reinforcement Learning (RL) framework that enables quadrupedal robots to dynamically adapt to both varying payloads and diverse terrains. The framework consists of a nominal policy responsible for baseline locomotion and an adaptive policy that learns corrective actions to preserve stability and improve command tracking under payload variations. We validate the proposed approach through large-scale simulation experiments in Isaac Gym and real-world hardware deployment on a Unitree Go1 quadruped. The controller was tested on flat ground, slopes, and stairs under both static and dynamic payload changes. Across all settings, our adaptive controller consistently outperformed the controller in tracking body height and velocity commands, demonstrating enhanced robustness and adaptability without requiring explicit gait design or manual tuning.