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About

Pilar Sobrevilla is a pioneering researcher at the intersection of artificial intelligence, computer vision, and medical robotics, with a particular focus on force estimation and sensory feedback in robotic-assisted minimally invasive surgery. Her work addresses one of the most pressing challenges in modern surgical robotics: the absence of haptic force feedback, which limits a surgeon's ability to gauge tissue interaction and prevent intraoperative complications. Sobrevilla's most significant contributions involve developing innovative sensorless force estimation frameworks that combine recurrent neural networks, neuro-fuzzy systems, and 3D vision-based reconstruction to replicate tactile sensing without the practical limitations of physical sensors. Her 2016 deep neuro-fuzzy approach and her 2014 recurrent neural network method — garnering 40 and 39 citations respectively — demonstrate both the breadth and consistency of her technical innovation. By integrating deep learning with fuzzy logic, she has enabled more human-like, uncertainty-aware systems capable of interpreting complex surgical environments. With over 148 cumulative citations across her key publications, Sobrevilla's research has meaningfully influenced the robotics and biomedical engineering communities. Her explorations into dimensionality reduction and specularity correction further reflect her commitment to robust, clinically viable AI-driven surgical solutions.