Giacomo Meanti
Papers
1
Total Citations
3
H-Index
1
About
Giacomo Meanti is a researcher at the forefront of robotic perception and efficient machine learning, with a particular focus on enabling real-time, adaptive visual systems for autonomous robots. His work bridges the gap between high-performance computer vision and the stringent computational constraints of embodied platforms like the iCub humanoid robot. Meanti’s major contribution lies in developing methods that allow robots to learn new visual tasks—such as instance segmentation—rapidly and with minimal data, without sacrificing accuracy. His most cited work, "Learn Fast, Segment Well: Fast Object Segmentation Learning on the iCub Robot" (2022), demonstrates a practical framework for fast adaptation in dynamic environments, a critical capability for robots operating in unstructured settings. While his citation count is still growing, reflecting the early stage of his career, the impact of his research is evident in its direct application to real-world robotic systems, where efficiency and speed are paramount. Meanti’s work is particularly notable for its focus on deployable, resource-aware AI, positioning him as a rising contributor to the fields of robot learning and efficient deep learning.
Research Focus
Key Achievements
Top Papers
- 1