Nathanael Hutama Harsono
Papers
1
Total Citations
2
H-Index
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About
Nathanael Hutama Harsono is a robotics and computer vision researcher whose work centers on pose estimation for humanoid robots, with a particular focus on bridging the gap between human motion analysis and robotic perception. His most notable contribution, the HiroPoseEstimation dataset (2023), provides a specialized resource for estimating poses of kid-size humanoid robots—a critical tool for advancing gesture control, health monitoring, and human-robot interaction. This dataset addresses a key gap in the field, where most pose estimation research targets human subjects, and has already garnered early citations for its novelty. Harsono’s research extends to applications in sign language understanding, elderly activity tracking, and sports analytics, demonstrating the versatility of pose estimation technology. By creating a benchmark tailored to humanoid platforms, he has enabled more accurate tracking of robotic movements, which is essential for developing responsive, autonomous systems. His work reflects a deep commitment to making robots more perceptive and adaptive, with potential impacts on assistive technologies and interactive robotics. As an emerging researcher, Harsono’s contributions are laying the groundwork for safer, more intuitive human-robot collaboration.
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Top Papers
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