Marco Campanella
Papers
2
Total Citations
84
H-Index
2
About
Marco Campanella is a pioneering researcher in the field of robotic tactile sensing and neural network-based control systems. His work centers on the critical challenge of enabling robots to detect and respond to incipient object slippage—a fundamental requirement for dexterous grasping and manipulation. Campanella’s major contributions include the development of skin-like tactile sensors integrated with neural network processing, allowing robots to anticipate and prevent object slippage before it occurs. His seminal 1998 paper, "Detection of incipient object slippage by skin-like sensing and neural network processing," has garnered 75 citations, underscoring its lasting influence on the design of artificial tactile systems. Building on this foundation, his 2002 study "Slip detection by a tactile neural network" further refined these methods, achieving 9 citations. Campanella’s work is notable for advancing the integration of tactile feedback with machine learning, a key step toward more autonomous and adaptive robotic hands. His research remains essential reading for engineers and scientists developing next-generation prosthetics, industrial grippers, and humanoid robots, where precise tactile perception is paramount.
Research Focus
Key Achievements
Top Papers
- 1
- 2Slip detection by a tactile neural network9 citations · 2002