Pavel Orlov

Papers

Total Citations

H-Index

About

Pavel Orlov is a researcher specializing in assistive robotics, human-machine interfaces, and gaze-based interaction systems. His work sits at the compelling intersection of neuroscience-inspired computing and rehabilitative technology, with a particular focus on restoring autonomy to individuals living with movement disabilities. Orlov's most influential contribution, "Gaze-based, context-aware robotic system for assisted reaching and grasping" (2019, 68 citations), demonstrates his ability to translate complex low-level robotic control into intuitive, high-level commands driven entirely by eye movement — a landmark step forward in assistive technology design. Building on this foundation, his earlier work on the "Gaze-Contingent Intention Decoding Engine" (2018) explored how natural eye-movement patterns encode grasp intention, revealing the potential of gaze as a rich, real-time communication channel between humans and machines. More recently, Orlov has embraced deep learning methodologies, applying neural networks to classify human action intentions directly from natural eye movements (2021), pushing the boundaries of non-invasive human-machine interaction. Across his body of work, Orlov consistently champions the idea that the eyes are not merely passive sensors, but active windows into human intention — a vision that continues to shape next-generation assistive and augmentation technologies.