A Receding Horizon Push Recovery Strategy for Balancing the iCub Humanoid Robot

TL;DR

This paper introduces a Model Predictive Control approach for the iCub humanoid robot to improve its push recovery capabilities by predicting future states and handling hybrid system constraints, enhancing robustness.

Contribution

It extends a previous capture point based push recovery method with a receding horizon control, enabling better prediction and handling of hybrid constraints for humanoid balance.

Findings

MPC improves push recovery robustness.

Simulation results validate the approach.

Enhanced handling of hybrid system constraints.

Abstract

Balancing and reacting to strong and unexpected pushes is a critical requirement for humanoid robots. We recently designed a capture point based approach which interfaces with a momentum-based torque controller and we implemented and validated it on the iCub humanoid robot. In this work we implement a Receding Horizon control, also known as Model Predictive Control, to add the possibility to predict the future evolution of the robot, especially the constraints switching given by the hybrid nature of the system. We prove that the proposed MPC extension makes the step-recovery controller more robust and reliable when executing the recovery strategy. Experiments in simulation show the results of the proposed approach.