Felice Andrea Pellegrino

About

Felice Andrea Pellegrino is a leading researcher at the intersection of robotics, control theory, and artificial intelligence, with a primary focus on bridging the critical gap between simulation and reality. His most influential work, the 2021 survey "Crossing the Reality Gap" (174 citations), has become a foundational reference for researchers applying Reinforcement Learning to real-world robot control. Pellegrino’s core contributions lie in developing robust and efficient controllers for complex robotic systems, from soft robots to collaborative manipulators. He has pioneered techniques that merge neuroevolution with neural network pruning to create controllers that are both computationally efficient and resilient, as demonstrated in his work on modular soft robots. His research also addresses fundamental challenges in robot positioning, inverse kinematics, and singularity avoidance, often employing convex programming and variational approaches. More recently, Pellegrino has advanced model-free kinematic control and visual servoing systems that operate without hand-eye calibration, pushing toward more autonomous and adaptable robotic platforms. His work consistently tackles the practical hurdles of deploying intelligent robots in constrained, real-world environments.

Research Focus

Key Achievements

Top Papers

1. 1
   Crossing the Reality Gap: A Survey on Sim-to-Real Transferability of Robot Controllers in Reinforcement Learning

   174 citations · 2021
2. 2
   On the effects of pruning on evolved neural controllers for soft robots

   15 citations · 2021
3. 3
   Enhancing Controller Performance for Robot Positioning in a Constrained Environment

   14 citations · 2008
4. 4
   Merging pruning and neuroevolution: towards robust and efficient controllers for modular soft robots

   11 citations · 2022
5. 5
   Inverse kinematics by means of convex programming: Some developments

   7 citations · 2015
6. 6
   Singularity Avoidance for Cart-Mounted Hand-Guided Collaborative Robots: A Variational Approach

   6 citations · 2022
7. 7
   Model-free kinematic control for robotic systems

   6 citations · 2024
8. 8
   Position-based visual servo control without hand-eye calibration

   4 citations · 2025
9. 9
   Hamiltonian path planning in constrained workspace

   4 citations · 2016
10. 10
    Characterization of Modeling Errors Affecting Performances of a Robotics Deep Reinforcement Learning Controller in a Sim-to-Real Transfer

    4 citations · 2021

Key Collaborators

Contact & Links