Papers
6
Total Citations
66
H-Index
4
About
Deepesh Kumar is a leading researcher at the intersection of neuromorphic engineering and tactile sensing, whose work is fundamentally reshaping how robots and prosthetics perceive touch. His primary research areas include neuromorphic tactile sensing, bio-inspired tactile processing, and sensorimotor integration for dexterous manipulation. Kumar’s major contribution lies in developing functional spiking neuronal networks that model the human tactile pathway from periphery to cortex, enabling machines to process edge orientation and texture with unprecedented biological realism. His landmark 2021 paper on this topic has garnered 29 citations, establishing a foundational framework for neuromorphic touch. He has also pioneered flexible tactile sensor arrays integrated with soft biomimetic fingers, achieving 18 citations for texture discrimination capabilities that bridge hardware and computational models. His work on spatiotemporal similarity for edge orientation estimation (12 citations) further demonstrates his innovative approach to tactile perception. Notably, Kumar’s recent SuperTac framework addresses the critical spatial-temporal resolution trade-off in tactile sensors through dimensionality reduction, promising breakthroughs for prosthetics and robotic manipulation. His hybrid frame-event vision solutions for grasp detection showcase his versatility in multimodal perception. With a growing citation impact and a clear trajectory toward bio-inspired, energy-efficient tactile systems, Kumar is a rising authority in neuromorphic sensing whose work directly informs next-generation prosthetic and robotic interfaces.
Research Focus
Key Achievements
Top Papers
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- 5Neuromorphic Tactile Sensing and Encoding2 citations · 2023
- 6SuperTac - tactile data super-resolution via dimensionality reduction1 citations · 2025