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Lorenzo Amatucci

7 accepted papers

2026

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

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 7SourcecodeScholar
2026

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

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…

2025

MUSE: A Real-Time Multi-Sensor State Estimator for Quadruped Robots

RA-L 2025

This letter introduces an innovative state estimator, MUSE (MUlti-sensor State Estimator), designed to enhance state estimation's accuracy and real-time performance in quadruped robot navigation. The proposed state estimator builds upon our previous work presented in (Fink et al. 2020). It integrate

Cited by 11SourceScholar
2025

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

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 8SourceScholar
2024

Accelerating Model Predictive Control for Legged Robots through Distributed Optimization

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 7SourcecodeScholar
2024

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

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 16SourcecodeScholar
2022

Monte Carlo Tree Search Gait Planner for Non-Gaited Legged System Control

ICRA 2022poster

In this work, a non-gaited framework for legged system locomotion is presented. The approach decouples the gait sequence optimization by considering the problem as a decision-making process. The redefined contact sequence problem is solved by utilizing a Monte Carlo Tree Search (MCTS) algorithm that…

Cited by 21SourceScholar