Shuvo Kumar Paul
Papers
3
Total Citations
39
H-Index
2
About
Shuvo Kumar Paul is a robotics researcher whose work lies at the intersection of computer vision, sensor fusion, and human–robot interaction. His most cited paper, “Object Detection and Pose Estimation from RGB and Depth Data for Real-Time, Adaptive Robotic Grasping” (2021, 26 citations), introduced a system that fuses visual and depth information to enable robots to dynamically perceive and manipulate objects—a critical step toward more autonomous and adaptable industrial and service robots. Building on this, Paul developed a multi-module interaction framework (2023, 12 citations) that integrates object recognition, verbal communication, user detection, and gesture/gaze recognition to make human–robot collaboration more natural and reliable. His most recent work (2024) combines speech and pointing gestures with skeleton-based hand tracking to precisely configure robotic tasks, further reducing ambiguity in human commands. Across these contributions, Paul has demonstrated a consistent focus on making robots not just smarter, but more intuitive partners for humans. His research is especially relevant for students and engineers interested in real-time perception systems, multimodal interfaces, and the practical deployment of collaborative robots in unstructured environments.
Research Focus
Key Achievements
Top Papers
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