Chien-Che Huang
Papers
2
Total Citations
11
H-Index
2
About
Chien-Che Huang is a robotics researcher specializing in intelligent manipulation and autonomous control for mobile manipulators. His work centers on integrating computer vision and deep learning to enable robots to perform complex pick-and-place tasks with minimal human intervention. Huang’s major contribution lies in developing end-to-end multi-task imitation learning frameworks that allow robots to learn visually guided control policies directly from raw image data. His 2019 paper on omnidirectional mobile manipulator control, which has garnered 8 citations, proposes a novel deep convolutional neural network architecture that unifies visual guidance and picking control into a single high-level system. Earlier, in 2018, he demonstrated a data-driven approach for 6-DoF manipulators, achieving autonomous picking using only visual input—a work that has accumulated 3 citations. Huang’s research bridges the gap between perception and action, advancing practical applications in industrial automation and service robotics. His achievements highlight a commitment to reducing reliance on hand-coded controllers, paving the way for more adaptive and intelligent robotic systems.
Research Focus
Key Achievements
Top Papers
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- 2