Diego Ferigo — accepted AI-conference papers

2026

Contact-Aware Morphology Optimization via Physically Consistent Differentiable Simulation

Filippo Luca Ferretti, Diego Ferigo, Alessandro Croci, Carlotta Sartore, Omar G. Younis, Silvio Traversaro +1

RA-L 2026

Optimizing robot morphology and behavior remains an open challenge, primarily because hardware parameters affect system and contact dynamics, and naive parametrization can yield physically inconsistent designs and unreliable gradients. We propose a simulation-based co-design approach for gradient-ba

Cited by 0 Source ↗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 0 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 ↗

2019

Learning to Sequence Multiple Tasks with Competing Constraints

Anqing Duan, Raffaello Camoriano, Diego Ferigo, Yanlong Huang, Daniele Calandriello, Lorenzo Rosasco +1

IROS 2019poster

Imitation learning offers a general framework where robots can efficiently acquire novel motor skills from demonstrations of a human teacher. While many promising achievements have been shown, the majority of them are only focused on single-stroke movements, without taking into account the problem o…

Cited by 8 Source ↗Scholar ↗

2018

Exploiting Friction in Torque Controlled Humanoid Robots

Gabriele Nava, Diego Ferigo, Daniele Pucci

IROS 2018poster

A common architecture for torque controlled humanoid robots consists in two nested loops. The outer loop generates desired joint/motor torques, and the inner loop stabilizes these desired values. In doing so, the inner loop usually compensates for joint friction phenomena, thus removing their inhere…

Cited by 3 Source ↗Scholar ↗