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Giuseppe L'Erario

11 accepted papers

2026

Robust by Co-Design: CAD-to-Control Evolutionary Optimization of Jet-Powered Humanoids

Punith Reddy Vanteddu, Davide Gorbani, Antonello Paolino, Giuseppe L'Erario, Hosameldin Awadalla Omer Mohamed, Fabio Bergonti +1
RA-L 2026

This letter presents a co-design and optimization framework for improving robustness in an aerial humanoid equipped with variable turbine configurations. The approach integrates CAD-based modeling, domain randomization, and multi-objective evolutionary optimization to jointly explore turbine placeme

Cited by 0Source ↗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

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 ↗
2022

Centroidal Aerodynamic Modeling and Control of Flying Multibody Robots

Tong Hui, Antonello Paolino, Gabriele Nava, Giuseppe L'Erario, Fabio Di Natale, Fabio Bergonti +2
ICRA 2022poster

This paper presents a modeling and control frame-work for multibody flying robots subject to non-negligible aero-dynamic forces acting on the centroidal dynamics. First, aero-dynamic forces are calculated during robot flight in different operating conditions by means of Computational Fluid Dynamics…

Cited by 7Source ↗Scholar ↗
2022

Momentum-Based Extended Kalman Filter for Thrust Estimation on Flying Multibody Robots

Hosameldin Awadalla Omer Mohamed, Gabriele Nava, Giuseppe L'Erario, Silvio Traversaro, Fabio Bergonti, Luca Fiorio +3
RA-L 2022

Effective control design of flying vehicles requires a reliable estimation of the propellers’ thrust forces to secure a successful flight. Direct measurements of thrust forces, however, are seldom available in practice and on-line thrust estimation usually follows from the application of fusion algo

Cited by 25Source ↗Scholar ↗
2022

Nonlinear Model Identification and Observer Design for Thrust Estimation of Small-scale Turbojet Engines

Affaf Junaid Ahamad Momin, Gabriele Nava, Giuseppe L'Erario, Hosameldin Awadalla Omer Mohamed, Fabio Bergonti, Punith Reddy Vanteddu +2
ICRA 2022poster

Jet-powered vertical takeoff and landing (VTOL) drones require precise thrust estimation to ensure adequate stability margins and robust maneuvering. Small-scale turbojets have become good candidates for powering heavy aerial drones. However, due to limited instrumentation available in these turboje…

Cited by 8Source ↗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 ↗
2020

Dynamic Control of a Rigid Pneumatic Gripper

Rocco Antonio Romeo, Luca Fiorio, Giuseppe L'Erario, Marco Maggiali, Giorgio Metta, Daniele Pucci
RA-L 2020

Pneumatic grippers are hugely employed in robotic applications. Nonetheless, their control is not easy due to difficulty in managing the pressure inside their air chambers. Pneumatic grippers have often simple structure though the lack of affordable control algorithms complicates their usage. Motiva

Cited by 8Source ↗Scholar ↗
2020

Modeling, Identification and Control of Model Jet Engines for Jet Powered Robotics

Giuseppe L'Erario, Luca Fiorio, Gabriele Nava, Fabio Bergonti, Hosameldin Awadalla Omer Mohamed, Emilio Benenati +2
RA-L 2020

The paper contributes towards the modeling, identification, and control of model jet engines. We propose a nonlinear, second order model in order to capture the model jet engines governing dynamics. The model structure is identified by applying sparse identification of nonlinear dynamics, and then t

Cited by 25Source ↗Scholar ↗