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About

Carmelo Sferrazza is a robotics researcher whose work sits at the intersection of tactile sensing, robot manipulation, and machine learning. He is best known for pioneering advances in vision-based optical tactile sensors — soft, camera-equipped surfaces that enable robots to perceive contact forces with remarkable richness and precision. His early landmark contribution, a finite element approach to generating ground truth force distributions for learning-based tactile sensing (75 citations), established a rigorous framework for training machine learning models on tactile data, addressing a fundamental bottleneck in the field. He further advanced the domain with a multi-camera tactile sensor design (55 citations) capable of capturing distributed contact information in real time. Sferrazza has consistently bridged sensing hardware with intelligent control, demonstrating zero-shot sim-to-real transfer for dynamic manipulation tasks (30 citations) and physics-based slip detection using distributed tactile signals (18 citations). More recently, his research has expanded toward multimodal robot learning, fusing vision and touch through masked learning frameworks and leveraging language grounding to incorporate heterogeneous sensors into generalist robot policies. His work even extends to healthcare, applying optical tactile sensing to unobtrusive sleep position classification. With nearly 200 citations across a decade of contributions, Sferrazza represents a leading voice in the push toward more perceptive, adaptable, and human-inspired robotic systems.