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.

翻译：开发复杂的控制架构已赋予机器人，特别是人形机器人，众多能力。然而，调谐这些架构仍然是一项需要专家干预的、具有挑战性且耗时的任务。在本工作中，我们提出了一种方法，用于自动调谐用于行走人形机器人的分层控制架构所有层的增益。我们通过采用不同的无梯度优化方法测试了我们的方法：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）。我们在仿真中和在真实的ergoCub人形机器人上验证了所找到的参数。我们的结果表明，遗传算法实现了最快的收敛速度（10 x 10^3 次函数评估 vs 其他算法所需的 25 x 10^3 次），并且在仿真中和迁移到真实机器人平台上时，完成任务的成功率均为100%。这些发现凸显了我们所提方法在自动化调参过程、减少人工干预需求方面的潜力。