Automatic Gain Tuning for Humanoid Robots Walking Architectures Using Gradient-Free Optimization Techniques

TL;DR

This paper presents an automated method for tuning humanoid robot walking control parameters using gradient-free optimization algorithms, significantly reducing manual effort and achieving high success rates in simulation and real-world tests.

Contribution

It introduces a novel automated tuning approach for hierarchical control architectures in humanoid robots using multiple gradient-free optimization techniques.

Findings

Genetic Algorithm converges faster than other methods.

100% success rate in simulation and real robot.

Effective transfer from simulation to real-world robot.

Abstract

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.