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About

Yainet Garcia-Garcia is a rising researcher at the forefront of intelligent robotic control and autonomous perception, with a focus on safety-critical applications. Her work bridges neural network theory and robust control, notably through the development of the Sliding Mode Control based on Closed-Form Continuous-Time Neural Networks with Gravity Compensation (SMC-CfC-G). This architecture enhances robotic precision by integrating continuous-time neural dynamics with sliding mode control, offering robust performance in uncertain environments. In the mining domain, she has advanced autonomous systems by designing a rock centroid localization system using Bird’s-Eye View images from the Time-of-Flight Blaze 101 camera, achieving robust 3D localization for safer, automated operations. Her most recent contribution, the Neuro-Visual Adaptive Control (NVAC) architecture, introduces semi-autonomous laparoscope guidance for robot-assisted surgery, directly integrating visual feedback with adaptive control to improve precision and safety. Each of her three most-cited papers has garnered 4 citations in 2024-2025, reflecting early but growing impact. Garcia-Garcia’s work is notable for its cross-domain applicability—from mining robotics to surgical assistance—demonstrating a versatile approach to embedding intelligence in physical systems.