Paolo Maria Viceconte — accepted AI-conference papers

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 0 Source ↗Scholar ↗

2024

Learning to Walk and Fly with Adversarial Motion Priors

Giuseppe L’Erario, Drew Hanover, Ángel Romero, Yunlong Song, Gabriele Nava, Paolo Maria Viceconte +2

IROS 2024poster

Robot multimodal locomotion encompasses the ability to transition between walking and flying, representing a significant challenge in robotics. This work presents an approach that enables automatic smooth transitions between legged and aerial locomotion. Leveraging the concept of Adversarial Motion…

Cited by 1 Source ↗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 22 Source ↗Scholar ↗

2021

On the Emergence of Whole-Body Strategies From Humanoid Robot Push-Recovery Learning

Diego Ferigo, Raffaello Camoriano, Paolo Maria Viceconte, Daniele Calandriello, Silvio Traversaro, Lorenzo Rosasco +1

RA-L 2021

Balancing and push-recovery are essential capabilities enabling humanoid robots to solve complex locomotion tasks. In this context, classical control systems tend to be based on simplified physical models and hard-coded strategies. Although successful in specific scenarios, this approach requires de

Cited by 22 Source ↗Scholar ↗