用无梯度优化自动调参，让仿人机器人走路更稳更快
Automatic Gain Tuning for Humanoid Robots Walking Architectures Using Gradient-Free Optimization Techniques
- 用遗传算法等无梯度方法自动调优层级控制架构参数
- 遗传算法仅需10^4次评估即收敛，成功率100%且可迁移到真实机器人
- 适合想减少人工调参的机器人控制研究者和工程师
开发复杂的控制架构已使机器人，特别是仿人机器人，具备多种能力。然而，这些架构的调参仍是一项耗时且需要专家介入的挑战性任务。本文提出一种方法，自动调优仿人机器人行走所用分层控制架构中所有层级的增益。我们采用多种无梯度优化方法进行测试：遗传算法（GA）、协方差矩阵自适应进化策略（CMA-ES）、进化策略（ES）和差分进化（DE）。在仿真环境和真实ergoCub仿人机器人平台上验证了找到的参数。结果表明，遗传算法在10×10³次函数评估内实现最快收敛，其他算法需25×10³次；且在仿真和真实平台上的任务完成率均为100%。这些发现凸显了该方法在自动化调参方面的潜力，显著降低对人工干预的需求。
原文摘要 · Abstract (English)
Developing sophisticated control architectures has endowed robots, particularly humanoid robots, with numerous capabilities. However, tuning these architectures remains a challenging and time-consuming task that requires expert intervention. In this work, we propose a methodology to automatically tune the gains of all layers of a hierarchical control architecture for walking humanoids. We tested our methodology by employing different gradient-free optimization methods: Genetic Algorithm (GA), Covariance Matrix Adaptation Evolution Strategy (CMA-ES), Evolution Strategy (ES), and Differential Evolution (DE). We validated the parameter found both in simulation and on the real ergoCub humanoid robot. Our results show that GA achieves the fastest convergence (10 x 10^3 function evaluations vs 25 x 10^3 needed by the other algorithms) and 100% success rate in completing the task both in simulation and when transferred on the real robotic platform. These findings highlight the potential of our proposed method to automate the tuning process, reducing the need for manual intervention.
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