Pablo Martinez Ulloa
Papers
2
Total Citations
23
H-Index
2
About
Pablo Martinez Ulloa is a robotics researcher whose work focuses on advancing tactile sensing for robotic manipulation. His primary research areas include optical tactile sensors, force and displacement measurement, and slip detection for improved robotic gripping. Ulloa’s major contributions center on developing novel, low-cost tactile sensing technologies that enhance a robot’s ability to interact with objects safely and securely. His most-cited work, "A Tactile Sensing Concept for 3-D Displacement and 3-D Force Measurement Using Light Angle and Intensity Sensing" (2023, 14 citations), introduces an innovative optical-based sensor design that uses a deformable silicone pillar and light sensing to infer both force and displacement in three dimensions. This concept stands out for its simple fabrication and compact form factor. Building on this, his paper "Robust Learning-Based Incipient Slip Detection Using the PapillArray Optical Tactile Sensor for Improved Robotic Gripping" (2023, 9 citations) tackles the critical challenge of detecting incipient slip—the earliest stage of an object slipping from a grasp. By combining the PapillArray sensor with machine learning, Ulloa’s work enables robots to take corrective action before dropping objects, significantly enhancing grip security. His research is paving the way for more dexterous and reliable robotic hands.
Research Focus
Key Achievements
Top Papers
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