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Stefano Dafarra

13 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

Maximising Tolerance to Disturbances via Combined Control-Actuation Optimisation for Robust Humanoid Robot Walking

Akhil Sathuluri, Carlotta Sartore, Stefano Dafarra, Silvio Traversaro, Markus Zimmermann, Daniele Pucci
RA-L 2025

Combined optimisation of various robot subsystems as a co-design problem has been shown to identify performant robots. However, classical optimisation methods result in point-optimum solutions that may not ensure robust performance and physical feasibility, i.e., the existence of components with spe

Cited by 1Source ↗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 ↗
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

Non-Linear Trajectory Optimization for Large Step-Ups: Application to the Humanoid Robot Atlas

Stefano Dafarra, Sylvain Bertrand, Robert J. Griffin, Giorgio Metta, Daniele Pucci, Jerry Pratt
IROS 2020poster

Performing large step-ups is a challenging task for a humanoid robot. It requires the robot to perform motions at the limit of its reachable workspace while straining to move its body upon the obstacle. This paper presents a non-linear trajectory optimization method for generating step-up motions. W…

Cited by 23Source ↗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 ↗
2018

A Control Architecture with Online Predictive Planning for Position and Torque Controlled Walking of Humanoid Robots

Stefano Dafarra, Gabriele Nava, Marie Charbonneau, Nuno Guedelha, Francisco Andrade, Silvio Traversaro +5
IROS 2018poster

A common approach to the generation of walking patterns for humanoid robots consists in adopting a layered control architecture. This paper proposes an architecture composed of three nested control loops. The outer loop exploits a robot kinematic model to plan the footstep positions. In the mid laye…

Cited by 20Source ↗Scholar ↗
2018

The CoDyCo Project Achievements and Beyond: Toward Human Aware Whole-Body Controllers for Physical Human Robot Interaction

Francesco Romano, Gabriele Nava, Morteza Azad, Jernej Camernik, Stefano Dafarra, Oriane Dermy +12
RA-L 2018

The success of robots in real-world environments is largely dependent on their ability to interact with both humans and said environment. The FP7 EU project CoDyCo focused on the latter of these two challenges by exploiting both rigid and compliant contacts dynamics in the robot control problem. Reg

Cited by 32Source ↗Scholar ↗