让四足机器人自动适应不同负载和地形，无需手动调参
MULE: Multi-terrain and Unknown Load Adaptation for Effective Quadrupedal Locomotion
- 用自适应强化学习动态调整运动策略，保持稳定
- 在平地、斜坡、楼梯上均优于传统方法，负载变化时仍能精准跟踪指令
- 适合需要灵活应对复杂环境的移动机器人研发者
四足机器人正广泛应用于多种地形的负重任务。现有基于模型预测控制的方法虽能处理负载变化，但常依赖预设步态或轨迹生成器，在非结构化环境中适应性差。为此，我们提出一种自适应强化学习框架，使四足机器人能同时应对负载与地形变化。该框架包含一个基础运动策略和一个学习修正动作的自适应策略，以维持稳定性并提升指令跟踪精度。我们在Isaac Gym中进行大规模仿真，并在Unitree Go1机器人上实现真实部署，测试了平坦地面、斜坡和楼梯上的静态与动态负载变化。所有场景下，自适应控制器在身体高度和速度指令跟踪上均显著优于基准方法，展现出更强鲁棒性和适应性，且无需显式设计步态或人工调参。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。