Shiao‐Li Tsao

National Yang Ming Chiao Tung University

Papers

Total Citations

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About

Shiao‐Li Tsao is a robotics and computer vision researcher whose work bridges perception and manipulation for autonomous systems. Her primary research areas include object detection, depth estimation, and robotic rearrangement, with a focus on enabling machines to interact with complex, real-world environments. Tsao’s major contributions include pioneering domain-specific approximations for faster object detection in autonomous driving and robotics, achieving a critical balance between accuracy and speed—a challenge highlighted in her 2018 paper (11 citations). She also advanced depth sensing by developing a deep depth fusion method for challenging objects like black, transparent, and reflective surfaces (2020, 8 citations), addressing a key limitation of structured-light and stereo cameras. In robotic manipulation, Tsao proposed an adaptive framework for rearranging unknown objects using low-cost tabletop robots (2020, 4 citations), eliminating the need for manual intermediate targets. Her work has been cited over 23 times, reflecting its impact on practical robotics and autonomous systems. Tsao’s research is notable for tackling real-world constraints—from sensor limitations to unknown object handling—making her a rising figure in applied robotics and computer vision.