G. Tevatia

Papers

Total Citations

417

H-Index

About

G. Tevatia is a pioneering researcher at the intersection of robotics and cognitive science, whose work uses humanoid robots as platforms to decode the principles of human behavior. With over 417 total citations, their most influential contribution, "Using humanoid robots to study human behavior" (266 citations), established a foundational framework for reciprocal advancement: programming robotic movement to mirror and test theories of human motor control, including trajectory formation, learning from demonstration, and oculomotor coordination. Tevatia’s second major work, "Inverse kinematics for humanoid robots" (143 citations), provided computationally efficient, real-time solutions for end-effector control—a critical step for enabling fluid, human-like motion in high-degree-of-freedom systems. Their lesser-cited but conceptually rich paper, "On-line Learning for Humanoid Robot Systems" (8 citations), championed supervised learning methods to overcome the nonlinearities that defy traditional analytical control, presaging today’s data-driven approaches in robotics. Tevatia’s research is notable for its interdisciplinary ambition: treating the robot not merely as a tool, but as a model organism for understanding biological movement. For students and researchers, Tevatia’s work offers a compelling blueprint for how embodied AI can illuminate both machine and human intelligence.