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

arXiv: https://arxiv.org/abs/2203.04489 · HTML: https://mlsanity.com/paper/2203.04489/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Mar 2022.

## Built on by (most cited first, 8 of 8)

- [Learning Bipedal Walking for Humanoids with Current Feedback](https://mlsanity.com/paper/2303.03724/) (2023)
- [StyleLoco: Generative Adversarial Distillation for Natural Humanoid Robot Locomotion](https://mlsanity.com/paper/2503.15082/) (2025)
- [RL-augmented MPC Framework for Agile and Robust Bipedal Footstep Locomotion Planning and Control](https://mlsanity.com/paper/2407.17683/) (2024)
- [Robot Crash Course: Learning Soft and Stylized Falling](https://mlsanity.com/paper/2511.10635/) (2025)
- [ZipMPC: Compressed Context-Dependent MPC Cost via Imitation Learning](https://mlsanity.com/paper/2507.13088/) (2025)
- [Accelerating and Scaling MPC-Guided Reinforcement Learning for Humanoid Locomotion and Manipulation](https://mlsanity.com/paper/2606.05687/) (2026)
- [PvP: Data-Efficient Humanoid Robot Learning with Proprioceptive-Privileged Contrastive Representations](https://mlsanity.com/paper/2512.13093/) (2025)
- [Goal-Conditioned Terminal Value Estimation for Real-time and Multi-task Model Predictive Control](https://mlsanity.com/paper/2410.04929/) (2024)

## Abstract (arXiv)

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.