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About

S. Doctolero is a robotics and control systems researcher whose work centers on the development of intelligent, adaptive control strategies for robotic manipulators, particularly in contexts requiring physical interaction with dynamic and unstructured environments. Doctolero's most recognized contribution, "Hybrid Force-Position Robot Control: An Artificial Neural Network Backstepping Approach" (2018, 5 citations), introduces an adaptive Lyapunov backstepping framework for achieving precise simultaneous force and position control in revolute-joint robots — a technically demanding problem with broad applications in industrial automation and human-robot collaboration. Building on this foundation, subsequent work explored CMAC neural network architectures for flexible-joint robots and addressed the often-overlooked challenge of seamlessly transitioning between free-space motion and surface contact without relying on switching schemes, contributing meaningful theoretical and practical advancements to the field. Perhaps most distinctively, Doctolero also investigated the novel intersection of personality psychology and control tuning, proposing that human personality types could inform how robotic systems adapt to unpredictable, human-inhabited environments. Though early in citation impact, this body of work demonstrates a creative and multidisciplinary research vision with strong implications for next-generation collaborative robotics.