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

TL;DR

This paper introduces an online non-linear centroidal MPC for humanoid robots that enables dynamic step adjustment and robust walking, validated through simulations and real robot experiments.

Contribution

It presents a novel centroidal MPC approach that considers full centroidal dynamics for online step adjustment in humanoid locomotion.

Findings

Successfully prevents robot from falling during walking and external pushes

Enables online adjustment of contact locations in real-time

Validates approach on simulation and real humanoid robot iCub

Abstract

This paper presents a Non-Linear Model Predictive Controller for humanoid robot locomotion with online step adjustment capabilities. The proposed controller considers the Centroidal Dynamics of the system to compute the desired contact forces and torques and contact locations. Differently from bipedal walking architectures based on simplified models, the presented approach considers the reduced centroidal model, thus allowing the robot to perform highly dynamic movements while keeping the control problem still treatable online. We show that the proposed controller can automatically adjust the contact location both in single and double support phases. The overall approach is then tested with a simulation of one-leg and two-leg systems performing jumping and running tasks, respectively. We finally validate the proposed controller on the position-controlled Humanoid Robot iCub. Results show that the proposed strategy prevents the robot from falling while walking and pushed with external forces up to 40 Newton for 1 second applied at the robot arm.