Gabriele Buondonno
Team Lead AI & Control - Control, Architecture, Product, Simulation
Paris, Ile-de-France
Summary
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Rockstar🎓
Top SchoolGabriele Buondonno is a robotics engineer and PhD who has spent eight years translating advanced control theory into deployable products, currently leading AI & Control, Architecture, Product and Simulation at Wandercraft to advance powered exoskeletons for people with reduced mobility. He combines deep academic experience—from a PhD and postdoc in humanoid robotics—with hands-on R&D and software development skills honed at Wandercraft and LAAS-CNRS. A core contributor to the widely used Pinocchio dynamics library, he specializes in legged locomotion, rigid-body dynamics optimization, and numerical robustness in low-level control code. Gabriele’s background spans both medical-device product constraints and high-performance computational improvements, reflecting a knack for turning mathematically complex algorithms into reliable, real-world systems. Based in Paris, he brings cross-cultural research experience (including Tohoku University) and a pragmatic focus on safety and simulation-driven design.
8 years of coding experience
8 years of employment as a software developer
Doctor of Philosophy - PhD, Mechatronics, Robotics, and Automation Engineering, Doctor of Philosophy - PhD, Mechatronics, Robotics, and Automation Engineering at Sapienza Università di Roma
Github Skills (6)
rigid-bodies10
c-language10
cprogramming-language10
linear-algebra10
dynamics10
computational-physics9
Programming languages (6)
Github contributions (5)
stack-of-tasks/pinocchio
Apr 2018 - Jan 2021
A fast and flexible implementation of Rigid Body Dynamics algorithms and their analytical derivatives
Role in this project:
Back-end Developer
Back-end DeveloperContributions:22 reviews, 312 commits, 87 PRs in 2 years 9 months
Contributions summary:Gabriele's contributions focused on fixing and improving the low-level code within the "pinocchio" library, primarily related to rigid body dynamics. Their work involved correcting type definitions for compatibility with specific compilers, re-implementing core calculation methods within a joint composite framework, and addressing bugs in the inertia calculation. These changes demonstrate a focus on optimizing the mathematical and computational performance of the library.
code-generationpythonautomatic-differentiationc-plus-pluspinocchio
gabrielebndn/eigenpy
Oct 2019 - Jan 2024
Bindings between Numpy and Eigen using Boost.Python
Contributions:18 pushes, 5 branches in 4 years 3 months
boost-pythonpythoneigennumpyboost
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