Jason Orlosky
Papers
4
Total Citations
61
H-Index
4
About
Jason Orlosky is a researcher whose work sits at the dynamic intersection of human-robot interaction, teleoperation, and extended reality (XR) technologies. His most influential contributions focus on solving the perceptual and navigational challenges that arise when humans remotely control drones and humanoid robots in complex environments. Orlosky's pioneering research on adaptive view management—explored across multiple studies with a combined citation count exceeding 60—has addressed critical problems such as occlusion, collision risk, and limited situational awareness during drone teleoperation in confined 3D structures like buildings and tunnels. His 2017 and 2019 papers on this topic established foundational frameworks for dynamically adjusting operator viewpoints to improve safety and precision in remote navigation tasks. Beyond drones, Orlosky has made notable contributions to humanoid robot teleoperation, developing panoramic view reconstruction techniques that leverage virtual and augmented reality to enhance remote presence. His 2018 investigation into throughput delay further deepened understanding of how latency affects head control precision and operator perception in monitoring tasks. Together, these contributions position Orlosky as a key voice in designing more intuitive, perceptually robust interfaces for the next generation of remote robotic systems.
Research Focus
Key Achievements
Top Papers
- 1Adaptive View Management for Drone Teleoperation in Complex 3D Structures19 citations · 2017
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