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Giulio Romualdi

15 accepted papers

2026

Adaptive Non-Linear Centroidal MPC with Stability Guarantees for Robust Locomotion of Legged Robots

Mohamed Elobaid, Giulio Turrisi, Lorenzo Rapetti, Giulio Romualdi, Stefano Dafarra, Tomohiro Kawakami +4
ICRA 2026poster

Nonlinear model predictive locomotion controllers based on the reduced centroidal dynamics are nowadays ubiquitous in legged robots. These schemes, even if they assume an inherent simplification of the robot’s dynamics, were shown to endow robots with a step-adjustment capability in reaction to smal…

Cited by 0Source ↗PDF ↗Scholar ↗
2025

Adaptive Non-Linear Centroidal MPC With Stability Guarantees for Robust Locomotion of Legged Robots

Mohamed Elobaid, Giulio Turrisi, Lorenzo Rapetti, Giulio Romualdi, Stefano Dafarra, Tomohiro Kawakami +4
RA-L 2025

Nonlinear model predictive locomotion controllers based on the reduced centroidal dynamics are nowadays ubiquitous in legged robots. These schemes, even if they assume an inherent simplification of the robot's dynamics, were shown to endow robots with a step-adjustment capability in reaction to smal

Cited by 10Source ↗Scholar ↗
2025

Online Nonlinear MPC for Multimodal Locomotion

Saverio Taliani, Gabriele Nava, Giuseppe L'Erario, Mohamed Elobaid, Giulio Romualdi, Daniele Pucci
ICRA 2025

Aerial humanoid robots can enhance the efficiency and safety of rescue operations in disaster scenarios. The control of such complex machines presents many challenges, for instance, the control of the different locomotion strategies and the stabilization of the transition maneuvers. In this article,

Cited by 1Source ↗Scholar ↗
2025

Physics-Informed Learning for Human Whole-Body Kinematics Prediction via Sparse IMUs

Cheng Guo, Giuseppe L'Erario, Giulio Romualdi, Mattia Leonori, Marta Lorenzini, Arash Ajoudani +1
IROS 2025

Accurate and physically feasible human motion prediction is crucial for safe and seamless human-robot collaboration. While recent advancements in human motion capture enable real-time pose estimation, the practical value of many existing approaches is limited by the lack of future predictions and co

Cited by 0Source ↗Scholar ↗
2025

Physics-Informed Neural Networks With Unscented Kalman Filter for Sensorless Joint Torque Estimation in Humanoid Robots

Ines Sorrentino, Giulio Romualdi, Lorenzo Moretti, Silvio Traversaro, Daniele Pucci
RA-L 2025

This paper presents a novel framework for whole-body torque control of humanoid robots without joint torque sensors, designed for systems with electric motors and high-ratio harmonic drives. The approach integrates Physics-Informed Neural Networks (PINNs) for friction modeling and Unscented Kalman F

Cited by 6Source ↗Scholar ↗
2025

Stabilizing Humanoid Robot Trajectory Generation via Physics-Informed Learning and Control-Informed Steering

Evelyn D'Elia, Paolo Maria Viceconte, Lorenzo Rapetti, Diego Ferigo, Giulio Romualdi, Giuseppe L'Erario +2
IROS 2025

Recent trends in humanoid robot control have successfully employed imitation learning to enable the learned generation of smooth, human-like trajectories from human data. While these approaches make more realistic motions possible, they are limited by the amount of available motion data, and do not

Cited by 0Source ↗Scholar ↗
2024

XBG: End-to-End Imitation Learning for Autonomous Behaviour in Human-Robot Interaction and Collaboration

Carlos Cardenas-Perez, Giulio Romualdi, Mohamed Elobaid, Stefano Dafarra, Giuseppe L'Erario, Silvio Traversaro +3
RA-L 2024

This letter presents XBG (eXteroceptive Behaviour Generation), a multimodal end-to-end Imitation Learning (IL) system for whole-body autonomous humanoid robots used in real-world Human-Robot Interaction (HRI) scenarios. The main contribution is an architecture for learning HRI behaviours using a dat

Cited by 6Source ↗code ↗Scholar ↗
2023

Online Non-linear Centroidal MPC for Humanoid Robots Payload Carrying with Contact-Stable Force Parametrization

Mohamed Elobaid, Giulio Romualdi, Gabriele Nava, Lorenzo Rapetti, Hosameldin Awadalla Omer Mohamed, Daniele Pucci
ICRA 2023poster

In this paper we consider the problem of allowing a humanoid robot that is subject to a persistent disturbance, in the form of a payload-carrying task, to follow given planned footsteps. To solve this problem, we combine an online nonlinear centroidal Model Predictive Controller - MPC with a contact…

Cited by 9Source ↗code ↗Scholar ↗
2022

ADHERENT: Learning Human-like Trajectory Generators for Whole-body Control of Humanoid Robots

Paolo Maria Viceconte, Raffaello Camoriano, Giulio Romualdi, Diego Ferigo, Stefano Dafarra, Silvio Traversaro +3
RA-L 2022

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Human-like</i> trajectory generation and footstep planning represent challenging problems in humanoid robotics. Recently, research in computer graphics investigated machine-learning methods for characte

Cited by 22Source ↗Scholar ↗
2022

Comparison of EKF-Based Floating Base Estimators for Humanoid Robots with Flat Feet

Prashanth Ramadoss, Giulio Romualdi, Stefano Dafarra, Silvio Traversaro, Daniele Pucci
IROS 2022poster

Extended Kalman filtering is a common approach to achieve floating base estimation of a humanoid robot. These filters rely on measurements from an Inertial Measurement Unit (IMU) and relative forward kinematics for estimating the base position-and-orientation and its linear velocity along with the a…

Cited by 0Source ↗Scholar ↗
2022

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

Giulio Romualdi, Stefano Dafarra, Giuseppe L'Erario, Ines Sorrentino, Silvio Traversaro, Daniele Pucci
ICRA 2022poster

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 biped…

Cited by 58Source ↗code ↗Scholar ↗
2021

DILIGENT-KIO: A Proprioceptive Base Estimator for Humanoid Robots using Extended Kalman Filtering on Matrix Lie Groups

Prashanth Ramadoss, Giulio Romualdi, Stefano Dafarra, Francisco Javier Andrade Chavez, Silvio Traversaro, Daniele Pucci
ICRA 2021poster

This paper presents a contact-aided inertial-kinematic floating base estimation for humanoid robots considering an evolution of the state and observations over matrix Lie groups. This is achieved through the application of a geometrically meaningful estimator which is characterized by concentrated G…

Cited by 14Source ↗Scholar ↗
2020

Whole-Body Walking Generation using Contact Parametrization: A Non-Linear Trajectory Optimization Approach

Stefano Dafarra, Giulio Romualdi, Giorgio Metta, Daniele Pucci
ICRA 2020poster

In this paper, we describe a planner capable of generating walking trajectories by using the centroidal dynamics and the full kinematics of a humanoid robot model. The interaction between the robot and the walking surface is modeled explicitly through a novel contact parametrization. The approach is…

Cited by 10Source ↗Scholar ↗