勾配不要最適化によるヒューマノイド歩行制御アーキテクチャの自動ゲイン調整
Automatic Gain Tuning for Humanoid Robots Walking Architectures Using Gradient-Free Optimization Techniques
ヒューマノイドの階層型歩行制御の全層ゲインを、遺伝的アルゴリズムなどの勾配不要最適化で自動調整する手法を提案し、シミュレーションと実機ergoCubで有効性を検証した。
著者: Carlotta Sartore, Marco Rando, Giulio Romualdi, Cesare Molinari, Lorenzo Rosasco, Daniele Pucci
分類: cs.RO
原文アブストラクト
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.
