通过强化学习实现可变刚度控制，提升机器人行走稳定性与能效。
Variable Stiffness for Robust Locomotion through Reinforcement Learning
- 将可变刚度纳入动作空间，支持按腿分组控制刚度。
- 在平坦地面训练后，仍能在复杂户外地形稳定行走。
- 无需手动调参，兼顾速度、抗扰与能耗表现。
强化学习使足式机器人能够完成高动态运动，但常需耗时的手动调校关节刚度。本文提出一种新控制范式，将可变刚度整合至动作空间，支持按关节（PJS）、按腿（PLS）及混合关节-腿（HJLS）的刚度分组控制。实验表明，采用按腿刚度（PLS）的可变刚度策略在速度追踪与抗推力恢复方面优于传统位置控制；而混合刚度（HJLS）则更节能。尽管策略仅在平坦地面训练，仍展现出在多种户外地形上的稳健行走能力，表明其具备强泛化性与良好的仿真到现实迁移能力。该方法简化了设计流程，无需逐关节调校刚度，同时在多项指标上保持竞争力。
原文摘要 · Abstract (English)
Reinforcement-learned locomotion enables legged robots to perform highly dynamic motions but often accompanies time-consuming manual tuning of joint stiffness. This paper introduces a novel control paradigm that integrates variable stiffness into the action space alongside joint positions, enabling grouped stiffness control such as per-joint stiffness (PJS), per-leg stiffness (PLS) and hybrid joint-leg stiffness (HJLS). We show that variable stiffness policies, with grouping in per-leg stiffness (PLS), outperform position-based control in velocity tracking and push recovery. In contrast, HJLS excels in energy efficiency. Despite the fact that our policy is trained on flat floor only, our method showcases robust walking behaviour on diverse outdoor terrains, indicating robust sim-to-real transfer. Our approach simplifies design by eliminating per-joint stiffness tuning while keeping competitive results with various metrics.
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