Christian Gianoglio
Papers
11
Total Citations
82
H-Index
5
About
Christian Gianoglio is a leading researcher at the intersection of robotics, embedded systems, and tactile sensing. His work focuses on enabling real-time perception of physical object properties—such as hardness, texture, and affordance—using resource-constrained devices. Gianoglio’s major contributions include developing computationally light machine learning algorithms that allow robotic grippers and prosthetic systems to classify object hardness and detect functional parts (affordances) directly on low-power embedded platforms. His 2023 paper on real-time hardness classification for robotic grippers has garnered 29 citations, highlighting its impact on practical tactile sensing. Notably, he has pioneered the use of piezoelectric-based biomimetic sensors combined with efficient feature extraction and shallow neural networks, achieving high accuracy without the heavy computational burden of deep learning. His work on hardware-aware affordance detection (16 citations) and data-driven grasp classification for low-power systems further underscores his commitment to bridging advanced perception with real-world deployment. Gianoglio’s research is pivotal for next-generation autonomous systems that must operate within strict power and processing constraints.
Research Focus
Key Achievements
Top Papers
- 1Embedded real-time objects’ hardness classification for robotic grippers29 citations · 2023
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- 4Data-Driven Video Grasping Classification for Low-Power Embedded System6 citations · 2019
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- 9Pilot Study: Experimental Analysis of PVDF Sensors Response to Slippage3 citations · 2025
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