Fabio Bergonti
Papers
10
Total Citations
78
H-Index
5
About
Fabio Bergonti is a robotics researcher whose work bridges aerial systems, humanoid robotics, and advanced control theory. His primary research areas span thrust estimation for flying vehicles, multimodal locomotion, co-design optimization, and aerodynamic modeling of complex robotic systems. Bergonti has made notable contributions to the estimation and control of flying multibody robots, including his widely recognized momentum-based Extended Kalman Filter for thrust estimation (23 citations), which addresses the practical challenge of inferring propeller forces without direct sensors. He extended this work to small-scale turbojet-powered VTOL drones, advancing stability and robustness in heavy aerial platforms. His research on whole-body trajectory optimization for multimodal robots tackles one of robotics' open challenges—enabling machines to seamlessly transition between aerial and legged locomotion. In humanoid robotics, Bergonti has explored ergonomic co-design using genetic algorithms, morphing covers for adaptive robot morphology, and jet-powered humanoid robot design that integrates full CAD geometry into optimization pipelines. More recently, he has leveraged Physics-Informed Neural Networks for friction identification and machine learning for aerodynamic control of flying humanoids. With over 75 total citations across a diverse and rapidly evolving portfolio, Bergonti represents an emerging voice at the intersection of aerial robotics and humanoid systems.
Research Focus
Key Achievements
Top Papers
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- 4Whole-Body Trajectory Optimization for Robot Multimodal Locomotion9 citations · 2022
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- 9Centroidal Aerodynamic Modeling and Control of Flying Multibody Robots4 citations · 2022
- 10Learning aerodynamics for the control of flying humanoid robots2 citations · 2025