Nilotpal Pramanik

Indian Institute of Information Technology Allahabad

Papers

Total Citations

H-Index

About

Nilotpal Pramanik is a researcher at the forefront of robotics and artificial intelligence, specializing in data-efficient learning for robotic manipulation. His work addresses a critical challenge in modern robotics: enabling robots to learn complex tasks like grasping objects with minimal training data. Pramanik’s most cited paper, "Generative model based robotic grasp pose prediction with limited dataset" (2022, 24 citations), introduces a novel approach that leverages generative models to predict optimal grasp poses, significantly reducing the need for large, costly datasets. This contribution has practical implications for industrial automation and assistive robotics, where data scarcity is common. By combining generative AI with robotic control, Pramanik’s research bridges the gap between theoretical machine learning and real-world robotic applications. His work has been recognized for its potential to democratize robotic learning, making advanced manipulation skills accessible in resource-constrained settings. With a focus on efficiency and generalization, Pramanik continues to push the boundaries of how robots perceive and interact with their environment, offering a promising path toward more adaptable and intelligent autonomous systems.