Papers
9
Total Citations
161
H-Index
5
About
Neelesh Kumar is a researcher working at the intersection of neuromorphic computing, deep reinforcement learning, and human-robot interaction, with a particular focus on energy-efficient robotics and brain-computer interfaces. His most influential contribution, "Reinforcement co-Learning of Deep and Spiking Neural Networks for Energy-Efficient Mapless Navigation with Neuromorphic Hardware" (2020, 80 citations), introduced a pioneering hybrid framework that combines deep and spiking neural networks to enable mobile robots to navigate unknown environments while dramatically reducing energy consumption — a critical challenge in real-world robotics. Building on this, his work on population-coded spiking neural networks for continuous control (35 citations) extended these neuromorphic principles to high-dimensional action spaces. Kumar has also made significant strides in neural decoding, developing interpretable deep neural networks to extract complex movement intentions from EEG signals for neurorehabilitation and prosthetics applications. His broader portfolio spans gait orthosis control, multimodal biosignal interfaces, smartwatch-based robot control, and EEG-adaptive rehabilitation robots, reflecting a coherent vision of intelligent, human-centered assistive technology. With over 160 cumulative citations, his work meaningfully bridges neuroscience-inspired computation and practical robotics engineering.
Research Focus
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Top Papers
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- 7iRoCo: Intuitive Robot Control From Anywhere Using a Smartwatch2 citations · 2024
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