Giuseppe Bucca
Papers
8
Total Citations
173
H-Index
6
About
Giuseppe Bucca is a leading researcher at the forefront of human–robot collaboration and intelligent manufacturing, with a focus on creating adaptive, human-centric production environments for Industry 4.0 and 5.0. His major contributions center on sensorless robot control, task learning, and uncertainty adaptation, where he leverages machine learning—particularly Bayesian Optimization—to enable robots to learn assembly tasks from human demonstration without external sensors. Bucca’s work on optimal switching impact/force controllers and hybrid impedance/admittance control allows manipulators to seamlessly adapt to varying environmental stiffness, enhancing safety and performance in collaborative settings. His highly cited 2020 paper on sensorless assembly task learning (77 citations) exemplifies his impact, while his human-centric framework (2023) places the worker at the center of production, shifting roles from repetitive tasks to supervision. Additional achievements include optimizing camera pose for object detection and estimating external joint torques for position-controlled robots. With over 170 total citations, Bucca’s research is instrumental in advancing intuitive, safe, and efficient human–robot collaboration for the factories of the future.
Research Focus
Key Achievements
Top Papers
- 1
- 2Sensorless Optimal Switching Impact/Force Controller23 citations · 2021
- 3A human-centric framework for robotic task learning and optimization23 citations · 2023
- 4
- 5
- 6
- 7
- 8