Tuan-Tang Le

National Taiwan University of Science and Technology

Papers

Total Citations

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About

Tuan-Tang Le is a robotics and computer vision researcher whose work bridges deep learning and 3D perception for industrial automation and autonomous systems. His primary research areas include robotic bin-picking, 6D pose estimation, and UAV autonomous landing—all centered on enabling machines to perceive and interact with their environments more reliably. Le’s most impactful contribution is a deep learning framework for random bin-picking of planar objects, demonstrated through a case study on USB packs (44 citations), which addresses the challenge of handling objects with insufficient geometric features for traditional methods. He further advanced object grasping with a combined deep learning and 3D vision approach for fast, accurate 6D pose estimation (35 citations). In aerial robotics, Le developed a landing area recognition system using deep learning (20 citations), tackling a critical safety bottleneck for UAV operations over populated areas. His work has accumulated over 99 citations, reflecting its relevance to both industrial manufacturing and autonomous logistics. By integrating neural networks with classical 3D vision techniques, Le has delivered practical solutions that improve robotic dexterity and UAV autonomy, making him a notable contributor to modern intelligent systems research.