Abhishek Das

Vellore Institute of Technology University

Papers

Total Citations

H-Index

About

Abhishek Das is a researcher whose work bridges deep learning and robotics, with a primary focus on object grasping and manipulation. His most cited paper, "Object Grasping using Convolutional Neural Networks" (2019), introduces a learning-based approach that enables a robotic arm to grasp objects or clear clutter using visual input. By leveraging a pre-trained AlexNet CNN on ImageNet, Das demonstrates how continuous image feeds can guide a deep neural network to identify and interact with objects in real time. This work, while early in its citation trajectory with 2 citations, lays foundational groundwork for integrating computer vision with robotic control systems. Das’s contributions are particularly relevant for students and researchers interested in the intersection of artificial intelligence and physical automation, offering a practical framework for using convolutional neural networks in robotic grasping tasks. His approach highlights the potential of transfer learning in robotics, making complex manipulation tasks more accessible and efficient. As the field of robotic perception continues to evolve, Das’s research serves as a stepping stone for developing more intelligent and autonomous systems capable of interacting with their environments.