U. Thomas

Papers

Total Citations

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About

U. Thomas is a leading researcher in the fields of computer vision, human-robot interaction, and 3D pose estimation. His most-cited work, "3D Hand and Object Pose Estimation for Real-time Human-robot Interaction" (2022), has garnered 1 citation and represents a key contribution to enabling intuitive, real-time collaboration between humans and robots. Thomas’s research focuses on developing robust algorithms that allow robots to perceive and interpret human hand gestures and object manipulation in three-dimensional space, a critical step toward seamless, safe, and efficient human-robot teamwork in manufacturing, healthcare, and service robotics. By advancing real-time pose estimation, his work addresses the challenge of bridging the gap between human dexterity and robotic precision. Thomas’s contributions are foundational for researchers and engineers working on interactive robotic systems, and his findings continue to influence the design of more responsive and adaptive robotic assistants. His efforts underscore the importance of accurate, low-latency perception in unlocking the full potential of collaborative robotics.