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

Carlotta Sartore, Marco Rando, Giulio Romualdi, Cesare Molinari, Lorenzo Rosasco, Daniele Pucci

TL;DR

The results show that GA achieves the fastest convergence and 100% success rate in completing the task both in simulation and when transferred on the real robotic platform, highlighting the potential of the proposed method to automate the tuning process, reducing the need for manual intervention.

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.

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

TL;DR

The results show that GA achieves the fastest convergence and 100% success rate in completing the task both in simulation and when transferred on the real robotic platform, highlighting the potential of the proposed method to automate the tuning process, reducing the need for manual intervention.

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.
Paper Structure (6 sections, 2 figures)

This paper contains 6 sections, 2 figures.

Table of Contents

  1. INTRODUCTION
  2. BACKGROUND
  3. Notation
  4. Modelling
  5. Walking Hierarchical Control Architecture
  6. Centroidal MPC

Figures (2)

  • Figure 1: The ergoCub robot walking with an optimized control architecture, defined by parameters identified through gradient-free techniques.
  • Figure 2: The walking hierarchical control architecture, tuned via gradient-free techniques, composed of Centroidal Predictive Control (MPC) for calculating desired contact point forces and velocities, Zero Moment Point (ZMP) and Center of Mass (CoM) Controller for computing reference CoM velocity $\dot{x}$, and Whole-body Quadratic Programming (QP) Kinematic Controller that translates the previous block references into robots reference velocities $\nu^*$. Each layer processes feedback (fbk) from the robot's sensors.