Gianfranco Fenu
Papers
10
Total Citations
212
H-Index
4
About
Dr. Gianfranco Fenu is a leading researcher in robotics and control systems, whose work bridges the critical gap between simulation and real-world application. His primary research areas include reinforcement learning for robot control, kinematic optimization, and safety-critical supervision for industrial automation. Dr. Fenu’s most impactful contribution is his highly cited survey on sim-to-real transferability of robot controllers (174 citations), which has become a foundational resource for researchers tackling the "reality gap" in deploying RL-trained robots. He has also pioneered novel approaches to inverse kinematics using convex programming and developed model-free control techniques for both traditional and cable-driven robots, significantly advancing the field’s ability to operate without precise system models. His work on singularity avoidance for collaborative robots and position-based visual servoing demonstrates a consistent focus on practical, deployable solutions. With a career spanning over a decade, Dr. Fenu has also contributed to safety supervision in steel industry robotics, showcasing the real-world impact of his research. His ongoing work continues to shape how robots learn, adapt, and operate autonomously in complex, constrained environments.
Research Focus
Key Achievements
Top Papers
- 1
- 2Inverse kinematics by means of convex programming: Some developments7 citations · 2015
- 3
- 4Model-free kinematic control for robotic systems6 citations · 2024
- 5Position-based visual servo control without hand-eye calibration4 citations · 2025
- 6Hamiltonian path planning in constrained workspace4 citations · 2016
- 7
- 8Safety critical supervision for steel industry robotic applications3 citations · 2012
- 9Model-free cable robot control2 citations · 2023
- 10