Lingtao Huang
Papers
9
Total Citations
75
H-Index
6
About
Lingtao Huang is a robotics and automation researcher whose work spans teleoperation systems, deep learning-based manipulation, and human-robot interaction. Over more than a decade of sustained scholarship, Huang has made significant contributions to the design and control of construction teleoperation robots, pioneering master-slave control architectures that incorporate force feedback, gravity compensation, and virtual reality environments to enable intuitive and precise remote operation. His early work on haptic feedback during soft-object grasping and hardness recognition in construction robots demonstrated a sophisticated understanding of sensory integration in real-world robotic systems. More recently, Huang has expanded into deep learning applications, with his 2021 paper on multi-object sorting garnering 26 citations and reflecting his engagement with cutting-edge computer vision techniques for unstructured industrial environments. His 2017 study on hydraulic teleoperation with gravity compensation, cited 12 times, further underscores his expertise in practical robotic deployment. Across publications in dynamic obstacle detection, neural network-based object recognition, and robot teaching methodologies, Huang has built a cohesive research identity centered on making robots safer, smarter, and more capable of operating alongside humans in complex, real-world settings.
Research Focus
Key Achievements
Top Papers
- 1Research on Multi-Object Sorting System Based on Deep Learning26 citations · 2021
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- 6Research on Robot Teaching for Complex Task6 citations · 2020
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- 9Object Recognition Using Multiple Neural Networks and Force Sensing2 citations · 2018