Papers
3
Total Citations
18
H-Index
2
About
N. Sundararajan’s research lies at the intersection of neural networks, adaptive control, and multi-robot systems, with a particular focus on reinforcement learning for autonomous decision-making. His foundational 1993 paper on selecting network and learning parameters for adaptive neural robotic control, which has accumulated 11 citations, established early frameworks for integrating neural architectures into robotic systems. More recently, Sundararajan has tackled the challenging domain of decentralized swarm robotics, as demonstrated in his 2020 work on context-aware deep Q-networks for cooperative reconnaissance without inter-robot communication—a critical problem for operations in communication-denied environments. His 2024 paper introduces an efficient reinforcement learning scheme for the confinement escape problem, further showcasing his ongoing contributions to autonomous navigation and swarm intelligence. Sundararajan’s work is particularly notable for addressing real-world constraints such as uncertainty, heterogeneity in targets, and communication limitations, making his research highly relevant for applications in search-and-rescue, surveillance, and multi-agent coordination. His cumulative citation impact reflects a sustained influence on adaptive control and reinforcement learning for robotics.
Research Focus
Key Achievements
Top Papers
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