G. Gandolfi
Papers
1
Total Citations
9
H-Index
1
About
G. Gandolfi is a researcher advancing the field of physical robot interaction (PRI), with a focus on integrating tactile sensing and predictive control for dexterous manipulation. Their most notable contribution is the development of **Deep Functional Predictive Control (deep-FPC)**, a data-driven framework that leverages tactile predictions to guide robot pushing of 3-D object clusters. This work, published in 2023, addresses a critical challenge in robotics: enabling robots to anticipate and adapt to the complex, non-linear dynamics of physical contact without relying on precise analytical models. By using a forward model trained on tactile feedback, Gandolfi’s approach allows robots to make informed, real-time adjustments, improving stability and success in tasks like cluster rearrangement. With 9 citations for this key paper, the work is gaining traction for its practical implications in manufacturing and service robotics. Gandolfi’s research bridges the gap between sensing and control, offering a pathway toward more autonomous and robust robotic systems capable of handling unstructured environments. Their contributions are particularly relevant for students and researchers interested in embodied AI, tactile perception, and model-predictive control.
Research Focus
Key Achievements
Top Papers
- 1