Giuseppe De Maria
Papers
8
Total Citations
187
H-Index
8
About
Giuseppe De Maria is a robotics researcher whose work centers on tactile and force sensing, human-robot interaction, and robotic manipulation. His most significant contributions lie in developing integrated sensing technologies that enable robots to perceive and respond to physical contact with both objects and humans. A recurring theme across his research is the insufficiency of single-modality sensing: his highly cited 2015 study (44 citations) demonstrated that combining force and tactile data is essential for reliable slipping detection and avoidance — a finding that has shaped subsequent approaches to dexterous robotic grasping. His earlier 2014 work on Kalman filter-based slip detection (31 citations) laid important algorithmic groundwork for this line of inquiry. Beyond manipulation, De Maria has advanced distributed tactile sensing for safe human-robot collaboration, with a 2017 paper (33 citations) proposing sensor arrays capable of both collision detection and intuitive physical interfacing. His research also extends into mechanical design, including optimized hexapod robot configurations for aeronautics applications. Through optoelectronic sensor development, data fusion strategies, and human observation methods for imitation learning, De Maria has consistently worked to bridge the sensing gap between human and robotic manipulation capabilities.
Research Focus
Key Achievements
Top Papers
- 1
- 2A Distributed Tactile Sensor for Intuitive Human-Robot Interfacing33 citations · 2017
- 3
- 4Slipping detection and avoidance based on Kalman filter31 citations · 2014
- 5Modeling and slipping control of a planar slider15 citations · 2020
- 6Tactile sensor for human-like manipulation14 citations · 2012
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- 8