Papers
5
Total Citations
62
H-Index
4
About
Gabriele Fadini is a robotics researcher specializing in computational co-design, legged locomotion, and robot optimization. His work sits at the intersection of mechanical design, control theory, and machine learning, with a particular focus on developing algorithmic frameworks that simultaneously optimize robot hardware and control strategies to maximize energy efficiency and task performance. Fadini's most influential contribution is his computational co-design framework for legged robots, which enables concurrent optimization of physical parameters — such as robot size and actuator selection — alongside control trajectories. This work, cited 31 times, has helped establish co-design as a rigorous computational discipline rather than an iterative engineering guessing game. His subsequent research extended these ideas through bi-level genetic algorithms for robust optimization (17 citations) and practical quadruped prototype development (6 citations), demonstrating that his methods translate meaningfully from simulation to hardware. More recently, Fadini has expanded into robotic manipulator design and loco-manipulation, combining model-based control with reinforcement learning in his RAMBO framework. Together, his body of work — accumulating over 60 citations — offers students and engineers powerful tools for automating and accelerating the design of next-generation robotic systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2Simulation Aided Co-Design for Robust Robot Optimization17 citations · 2022
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- 5Rambo: RL-Augmented Model-Based Whole-Body Control for Loco-Manipulation2 citations · 2025