Josh Chen Ye Seng

Agency for Science, Technology and Research

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Total Citations

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About

Josh Chen Ye Seng is a leading researcher in intelligent robotic automation for manufacturing, with a focus on adaptive toolpath generation, deep learning-driven process planning, and AI-enabled soft grasping. His most influential work, “Adaptive Automatic Robot Tool Path Generation Based on Point Cloud Projection Algorithm” (2019, 26 citations), addresses a critical industry bottleneck by replacing manual polishing and masking with automated, sensor-guided robotic trajectories. This contribution significantly reduces human labor in repetitive, high-precision tasks. Building on this, his 2021 paper on “Automatic Toolpath Pattern Recommendation for Various Industrial Applications based on Deep Learning” (7 citations) pioneered the use of neural networks to recommend optimal toolpath patterns, enabling flexible automation across diverse processes like deburring and masking. Most recently, his 2022 study on “AI-Enabled Soft Versatile Grasping for High-Mixed-Low-Volume Applications with Tactile Feedbacks” (3 citations) tackles the challenge of handling mass-customized products by integrating tactile feedback with soft grippers, allowing robots to adapt to novel objects without reprogramming. Collectively, Seng’s work bridges the gap between rigid industrial robotics and the flexibility required for modern, high-mix manufacturing, earning him recognition as a key innovator in smart factory automation.