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This paper presents a Non-Linear Model Predictive Controller for humanoidrobot locomotion with online step adjustment capabilities. The proposedcontroller considers the Centroidal Dynamics of the system to compute thedesired contact forces and torques and contact locations. Differently frombipedal walking architectures based on simplified models, the presentedapproach considers the reduced centroidal model, thus allowing the robot toperform highly dynamic movements while keeping the control problem stilltreatable online. We show that the proposed controller can automatically adjustthe contact location both in single and double support phases. The overallapproach is then tested with a simulation of one-leg and two-leg systemsperforming jumping and running tasks, respectively. We finally validate theproposed controller on the position-controlled Humanoid Robot iCub. Resultsshow that the proposed strategy prevents the robot from falling while walkingand pushed with external forces up to 40 Newton for 1 second applied at therobot arm.

Online Non-linear Centroidal MPC for Humanoid Robot Locomotion with Step Adjustment