S. K. Paul
Papers
1
Total Citations
3
H-Index
1
About
S. K. Paul is a researcher specializing in robotic vision, machine perception, and intelligent autonomous systems. Their work sits at the intersection of computer vision and robotics, with a particular focus on enabling robots to perceive and interact with their environments more effectively. Paul's most recognized contribution, "Object Detection and Pose Estimation from RGB and Depth Data for Real-time, Adaptive Robotic Grasping" (2021), addresses one of the fundamental challenges in modern robotics: equipping machines with the ability to identify objects and accurately determine their spatial orientation in real time. By leveraging both RGB and depth data, this research advances the capability of robots to perform adaptive grasping — a critical step toward practical deployment in dynamic, unstructured environments such as warehouses, hospitals, and homes. With 3 citations since its publication, the work is establishing early traction within the robotics and computer vision communities. Paul's research contributes meaningfully to the broader goal of making autonomous robotic systems more reliable, perceptually aware, and capable of human-like dexterity, making their work of growing interest to students and researchers pursuing advances in intelligent robotics and real-world automation.
Research Focus
Key Achievements
Top Papers
- 1
Key Collaborators
Related papers
- Object Detection and Pose Estimation from RGB and Depth Data for Real-time, Adaptive Robotic Grasping
- Object Detection and Pose Estimation from RGB and Depth Data for Real-time, Adaptive Robotic Grasping
- Object Detection and Pose Estimation from RGB and Depth Data for Real-Time, Adaptive Robotic Grasping
- 6D Pose Estimation with Combined Deep Learning and 3D Vision Techniques for a Fast and Accurate Object Grasping
- Anchor-Based Multi-Scale Deep Grasp Pose Detector With Encoded Angle Regression
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