Sheng-Lin Lai

About

Sheng-Lin Lai is a leading researcher in human-robot interaction (HRI) and intelligent robotics, with a focus on deep learning-driven perception systems. His most influential work, "Interactions Between Specific Human and Omnidirectional Mobile Robot Using Deep Learning Approach: SSD-FN-KCF" (2020), has garnered 13 citations and addresses a critical challenge in HRI: enabling robots to reliably detect and track specific individuals in dynamic environments. Lai pioneered the integration of Single-Shot Detection (SSD), FaceNet, and Kernelized Correlation Filter (KCF) into a unified framework, achieving robust, real-time person identification and tracking for omnidirectional mobile robots. This contribution bridges computer vision and robotics, enhancing robots' ability to interact naturally with humans in settings like healthcare, service, and manufacturing. By tackling the "specific human" detection problem, Lai's work lays a foundation for more intuitive and safer human-robot collaboration. His research continues to influence the development of autonomous systems that can seamlessly operate alongside people, marking him as a key innovator in the field of intelligent robotics.

Research Focus

Key Achievements

Top Papers

1. 1
   Interactions Between Specific Human and Omnidirectional Mobile Robot Using Deep Learning Approach: SSD-FN-KCF

   13 citations · 2020

Key Collaborators

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