Papers
2
Total Citations
31
H-Index
2
About
Manish Kumar is a researcher whose work spans robotics, computer vision, and deep learning, with a particular focus on solving complex real-world engineering and surveillance challenges. His 2016 paper, "A Note on Mechanical Feasibility Predicate for Robotic Assembly Sequence Generation," has garnered 25 citations, establishing him as a contributor to the field of robotic automation and assembly planning — an area critical to advancing intelligent manufacturing systems. This work addresses the foundational logic governing how robots determine viable sequences for assembling components, a problem central to industrial robotics. More recently, Kumar has extended his expertise into the rapidly evolving domain of human action recognition. His 2023 work applying computer vision and deep learning techniques to smart video monitoring tackles a particularly demanding challenge: efficiently detecting and interpreting human movement within high-resolution surveillance footage. Early citation interest in this paper signals growing relevance as demand for intelligent security systems increases. Taken together, Kumar's research reflects a consistent drive to apply computational intelligence to physically grounded problems — from factory floors to surveillance networks — making his work valuable to students and professionals in robotics, AI, and computer vision alike.
Research Focus
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Top Papers
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