Vignesh Narayanan

Washington University in St. Louis, Arizona State University, Missouri University of Science and Technology

Papers

Total Citations

122

H-Index

About

Vignesh Narayanan’s research lies at the intersection of adaptive control, neural networks, and human-robot teaming, with a focus on making robotic systems both smarter and more efficient. His most influential work, an event-sampled output feedback control framework for robot manipulators (68 citations), pioneered the use of adaptive neural networks to reconstruct joint velocities from limited data, dramatically reducing computational load while maintaining precise trajectory tracking. This breakthrough addresses a fundamental challenge in robotics: how to achieve real-time performance under resource constraints. Narayanan’s contributions extend beyond algorithms into human factors, where his 2015 study (35 citations) systematically evaluated the long-held assumption that proactive robot assistance improves teaming—finding that the accuracy of a robot’s goal inference critically determines whether proactive support helps or hinders human partners. His work on automated planning for peer-to-peer robot teaming (15 citations) further advanced remote human-robot interaction by endowing robots with general planning capabilities rather than limiting them to supervisory roles. Through these contributions, Narayanan has shaped how researchers design adaptive, event-driven controllers and understand the nuanced dynamics of human-robot collaboration, making his work essential reading for anyone interested in the future of autonomous systems.