Mohit Sambrani

Vellore Institute of Technology University

Papers

Total Citations

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About

Mohit Sambrani’s research lies at the intersection of robotics, computer vision, and deep learning, with a primary focus on enabling intelligent object manipulation. His most cited work, “Object Grasping using Convolutional Neural Networks” (2019), introduces a learning-based approach that empowers a robotic arm to autonomously grasp objects or clear clutter in unstructured environments. By leveraging a pre-trained AlexNet model from ImageNet, Sambrani’s system continuously processes visual input through a deep neural network, allowing the robot to identify and interact with objects in real time. This contribution addresses a fundamental challenge in robotics—bridging perception and action—and has garnered attention for its practical, scalable methodology. Though early in his career, Sambrani’s work demonstrates a strong commitment to integrating state-of-the-art neural architectures with robotic control, paving the way for more adaptive and autonomous systems. His research holds promise for applications in manufacturing, assistive robotics, and household automation, where reliable object grasping remains a critical bottleneck.