Narayan Srinivasa
University of Illinois Urbana-Champaign, HRL Laboratories (United States)
Papers
7
Total Citations
37
H-Index
4
About
Narayan Srinivasa is a pioneering researcher at the intersection of robotics, active vision, and bio-inspired control systems. His work fundamentally addresses how robots can perceive and interact with their environments through self-organizing, adaptive mechanisms. Srinivasa’s major contributions lie in developing neural network frameworks that enable robots to learn and control movement without explicit programming, particularly in redundant systems. His influential 1998 paper on efficient learning of VAM-based 3D target representation (9 citations) laid groundwork for invariant visual perception, while his 2014 methodology for controlling motion and constraint forces in holonomically constrained systems (8 citations) advanced robotic manipulation theory. His bio-inspired kinematic controllers (2012, 7 citations) demonstrated how robots can autonomously avoid obstacles during reaching tasks, mimicking biological motor learning. Notably, his 1998 framework for active vision-based robot control (5 citations) addressed the critical challenge of integrating visual feedback into control loops without precise calibration. Srinivasa’s self-organizing neural models for fault-tolerant control (2007, 3 citations) further showcased his commitment to robust, adaptive robotic systems. His work continues to influence modern approaches to autonomous robot learning and perception.
Research Focus
Key Achievements
Top Papers
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- 4A framework for active vision-based robot control using neural networks5 citations · 1998
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