A Receding Horizon Push Recovery Strategy for Balancing the iCub Humanoid Robot
This addresses push recovery for humanoid robots like iCub, but appears incremental as it extends an existing method.
The authors tackled the problem of humanoid robot push recovery by implementing a Receding Horizon (Model Predictive Control) extension to a capture point-based approach, proving it makes step-recovery more robust and reliable in simulation.
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.