Papers
9
Total Citations
57
H-Index
4
About
Lorenzo Amatucci is a robotics researcher whose work is driving advances in legged locomotion, with a particular focus on quadrupedal robots. His research centers on developing novel control and planning algorithms that enable these machines to navigate complex, unstructured terrains with greater agility and autonomy. A key contribution is his pioneering use of Monte Carlo Tree Search (MCTS) for non-gaited locomotion, decoupling gait sequence optimization as a decision-making process to allow for more versatile movement. Amatucci has also made significant strides in computational efficiency, accelerating Model Predictive Control (MPC) through distributed optimization and GPU parallelization, as demonstrated in his works on ADMM-based decomposition and Primal-Dual iLQR. His most cited paper (15 citations) establishes the MCTS gait planner, while his 2024 paper on distributed optimization (13 citations) is rapidly gaining influence. Beyond theoretical advances, Amatucci has a strong applied streak, leading the VERO project—a quadruped robot equipped with a vacuum cleaner for autonomous litter removal—showcasing his commitment to real-world impact. His work on the MUSE state estimator further highlights his focus on robust, real-time perception for practical deployment.
Research Focus
Key Achievements
Top Papers
- 1Monte Carlo Tree Search Gait Planner for Non-Gaited Legged System Control15 citations · 2022
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- 5MUSE: A Real-Time Multi-Sensor State Estimator for Quadruped Robots4 citations · 2025
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- 9Fast Convex Visual Foothold Adaptation for Quadrupedal Locomotion2 citations · 2022