Yu-Cheng Lai
Papers
5
Total Citations
65
H-Index
4
About
Yu-Cheng Lai is a robotics researcher whose work centers on intelligent manipulation, deep learning–driven visual perception, and autonomous grasping for industrial and service robots. His most influential contribution, “Visual Object Recognition and Pose Estimation Based on a Deep Semantic Segmentation Network” (45 citations), pioneered a deep learning architecture that enables robot manipulators to recognize and estimate the pose of random objects for pick-and-place tasks. He further advanced the field with “Visually Guided Picking Control of an Omnidirectional Mobile Manipulator Based on End-to-End Multi-Task Imitation Learning” (8 citations), introducing a CNN-based control system that learns visual guidance and picking actions jointly. Lai has also integrated optimization algorithms with vision, as seen in his work combining Ant Colony Optimization with image models for 6-DOF manipulator control (5 citations). His recent research on “Manipulability-Aware Task-Oriented Grasp Planning” (4 citations) addresses the challenge of planning grasps for redundant dual-arm robots while respecting joint limits and singularities. Across his publications, Lai demonstrates a consistent focus on bridging perception and control, with applications ranging from random object picking to dexterous dual-arm manipulation.
Research Focus
Key Achievements
Top Papers
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