Papers
9
Total Citations
68
H-Index
3
About
Mohit Vohra is a robotics researcher whose work spans robotic manipulation, computer vision, and autonomous systems, with particular emphasis on enabling robots to intelligently interact with unstructured, real-world environments. He is best known for his pioneering contributions to vision-based grasping of novel objects, most notably his 2019 paper on real-time grasp pose estimation in densely cluttered environments, which has garnered 38 citations and addressed longstanding limitations of conventional centroid- and axis-based grasping strategies. Complementing this, his domain-independent unsupervised approaches to grasp region detection offer computationally efficient alternatives to CNN-heavy methods, reflecting a consistent drive toward practical, deployable robotics solutions. Beyond grasping, Vohra has demonstrated impressive breadth, tackling challenges in construction automation, single-shot imitation learning, autonomous underground utility mapping, and insulator maintenance robotics. His end-to-end visual perception framework for wall construction and his work on visually guided unmanned ground vehicles in GPS-denied environments further highlight his commitment to bridging perception and autonomous action in complex settings. With a cumulative citation count exceeding 65 across his published works, Vohra's research portfolio reflects a researcher steadily building influence at the intersection of intelligent robotics, machine learning, and real-world automation challenges.
Research Focus
Key Achievements
Top Papers
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- 5Robot learning by Single Shot Imitation for Manipulation Tasks3 citations · 2022
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- 9Domain Independent Unsupervised Learning to grasp the Novel Objects2 citations · 2020