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Giulio Turrisi

14 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 ↗
2026

Feedback-MPPI: Fast Sampling-Based MPC Via Rollout Differentiation – Adios Low-Level Controllers

Tommaso Belvedere, Michael Ziegltrum, Giulio Turrisi, Valerio Modugno
ICRA 2026poster

Model Predictive Path Integral control is a powerful sampling-based approach suitable for complex robotic tasks due to its flexibility in handling nonlinear dynamics and non-convex costs. However, its applicability in real-time, high-frequency robotic control scenarios is limited by computational de…

Cited by 0Source ↗PDF ↗Scholar ↗
2026

Feedback-MPPI: Fast Sampling-Based MPC via Rollout Differentiation - Adios Low-Level Controllers

Tommaso Belvedere, Michael Ziegltrum, Giulio Turrisi, Valerio Modugno
RA-L 2026

Model Predictive Path Integral control is a powerful sampling-based approach suitable for complex robotic tasks due to its flexibility in handling nonlinear dynamics and non-convex costs. However, its applicability in real-time, high-frequency robotic control scenarios is limited by computational de

Cited by 5Source ↗Scholar ↗
2026

Primal-Dual iLQR for GPU-Accelerated Learning and Control in Legged Robots

Lorenzo Amatucci, João Sousa Pinto, Giulio Turrisi, Dominique Orban, Victor Barasuol, Claudio Semini
RA-L 2026

This paper introduces a novel Model Predictive Control (MPC) implementation for legged robot locomotion that leverages GPU parallelization. Our approach enables both temporal and state-space parallelization by incorporating a parallel associative scan to solve the primal-dual Karush-Kuhn-Tucker (KKT

Cited by 7Source ↗code ↗Scholar ↗
2026

Primal-Dual iLQR for GPU-Accelerated Learning and Control in Legged Robots

Lorenzo Amatucci, Joao Moreira de Sousa Pinto, Giulio Turrisi, Dominique Orban, Victor Barasuol, Claudio Semini
ICRA 2026poster

This paper introduces a novel Model Predictive Control (MPC) implementation for legged robot locomotion that leverages GPU parallelization. Our approach enables both temporal and state-space parallelization by incorporating a parallel associative scan to solve the primal-dual Karush-Kuhn-Tucker (KKT…

Cited by 0Source ↗PDF ↗code ↗Scholar ↗
2026

Proprioceptive Image: An Image Representation of Proprioceptive Data from Quadruped Robots for Contact Estimation Learning

Gabriel Fischer Abati, João Carlos Virgolino Soares, Giulio Turrisi, Victor Barasuol, Claudio Semini
ICRA 2026poster

This paper presents a novel approach for representing proprioceptive time-series data from quadruped robots as structured two-dimensional images, enabling the use of convolutional neural networks for learning locomotion-related tasks. The proposed method encodes temporal dynamics from multiple propr…

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

Non-Gaited Legged Locomotion With Monte-Carlo Tree Search and Supervised Learning

Ilyass Taouil, Lorenzo Amatucci, Majid Khadiv, Angela Dai, Victor Barasuol, Giulio Turrisi +1
RA-L 2025

Legged robots are able to navigate complex terrains by continuously interacting with the environment through careful selection of contact sequences and timings. However, the combinatorial nature behind contact planning hinders the applicability of such optimization problems on hardware. In this work

Cited by 8Source ↗Scholar ↗
2024

Accelerating Model Predictive Control for Legged Robots through Distributed Optimization

Lorenzo Amatucci, Giulio Turrisi, Angelo Bratta, Victor Barasuol, Claudio Semini
IROS 2024poster

This paper presents a novel approach to enhance Model Predictive Control (MPC) for legged robots through Distributed Optimization. Our method focuses on decomposing the robot dynamics into smaller, parallelizable subsystems, and utilizing the Alternating Direction Method of Multipliers (ADMM) to ens…

Cited by 7Source ↗code ↗Scholar ↗
2024

Leveraging Symmetry in RL-based Legged Locomotion Control

Zhi Su, Xiaoyu Huang, Daniel Ordoñez-Apraez, Yunfei Li, Zhongyu Li, Qiayuan Liao +5
IROS 2024poster

Model-free reinforcement learning is a promising approach for autonomously solving challenging robotics control problems, but faces exploration difficulty without information about the robot’s morphology. The under-exploration of multiple modalities with symmetric states leads to behaviors that are…

Cited by 10Source ↗Scholar ↗
2024

On the Benefits of GPU Sample-Based Stochastic Predictive Controllers for Legged Locomotion

Giulio Turrisi, Valerio Modugno, Lorenzo Amatucci, Dimitrios Kanoulas, Claudio Semini
IROS 2024

Quadrupedal robots excel in mobility, navigating complex terrains with agility. However, their complex control systems present challenges that are still far from being fully addressed. In this paper, we introduce the use of Sample-Based Stochastic control strategies for quadrupedal robots, as an alt

Cited by 16Source ↗code ↗Scholar ↗
2024

PACC: A Passive-Arm Approach for High-Payload Collaborative Carrying with Quadruped Robots Using Model Predictive Control

Giulio Turrisi, Lucas Schulze, Vivian S. Medeiros, Claudio Semini, Victor Barasuol
IROS 2024poster

In this paper, we introduce the concept of using passive arm structures with intrinsic impedance for robot-robot and human-robot collaborative carrying with quadruped robots. The concept is meant for a leader-follower task and takes a minimalist approach that focuses on exploiting the robots’ payloa…

Cited by 4Source ↗Scholar ↗
2023

Quadrupedal Footstep Planning Using Learned Motion Models of a Black-Box Controller

Ilyass Taouil, Giulio Turrisi, Daniel Schleich, Victor Barasuol, Claudio Semini, Sven Behnke
IROS 2023poster

Legged robots are increasingly entering new domains and applications, including search and rescue, inspection, and logistics. However, for such a systems to be valuable in real-world scenarios, they must be able to autonomously and robustly navigate irregular terrains. In many cases, robots that are…

Cited by 1Source ↗Scholar ↗
2022

On-Line Learning for Planning and Control of Underactuated Robots With Uncertain Dynamics

Giulio Turrisi, Marco Capotondi, Claudio Gaz, Valerio Modugno, Giuseppe Oriolo, Alessandro De Luca
RA-L 2022

We present an iterative approach for planning and controlling motions of underactuated robots with uncertain dynamics. At its core, there is a learning process which estimates the perturbations induced by the model uncertainty on the active and passive degrees of freedom. The generic iteration of th

Cited by 15Source ↗Scholar ↗