Mey Goh
Papers
2
Total Citations
12
H-Index
2
About
Mey Goh is a researcher focused on advancing robotic skill acquisition and automated manufacturing systems. Her primary research areas include imitation learning for assembly tasks and robotic repair technologies. Goh’s major contribution lies in developing symbolic-based recognition methods for contact states, enabling robots to learn complex assembly skills from human demonstrations—a critical step toward automating industrial processes that traditionally require manual dexterity. Her most-cited work, “Symbolic-Based Recognition of Contact States for Learning Assembly Skills” (2019, 9 citations), addresses the challenge of transferring human expertise to robots by distinguishing subtle contact states during assembly, paving the way for more adaptive and intelligent manufacturing. Additionally, Goh has explored automated rail repair systems, using laser line scanners to generate robotic deposition paths for surface defect remediation—a technology that could save the UK rail industry approximately £4 million annually. Her work bridges the gap between theoretical machine learning and practical industrial applications, demonstrating how robotics can enhance efficiency and traceability in maintenance and assembly. Goh’s research is particularly valuable for students and engineers interested in the intersection of robotics, manufacturing, and skill transfer.
Research Focus
Key Achievements
Top Papers
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- 2