Liam Tan

Papers

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About

Liam Tan is a rising star in robotics and artificial intelligence, whose work focuses on the intersection of imitation learning and complex manipulation. His research addresses one of the field’s most persistent challenges: teaching robots to perform multi-stage tasks that require handling deformable objects. Tan’s landmark paper, “Multistage Cable Routing Through Hierarchical Imitation Learning” (2024), has already garnered 46 citations, signaling its immediate impact. In this work, he introduced a hierarchical framework that enables robots to learn and execute intricate cable routing—a task involving threading a deformable cable through a series of clips. This contribution is significant not only for its technical novelty but also for its broader implications: it provides a scalable approach to automating assembly, wiring, and manufacturing processes that have long resisted robotic solutions. By breaking down complex, sequential manipulations into learnable sub-skills, Tan has opened new pathways for deploying robots in real-world industrial settings. His work is widely recognized for bridging the gap between theoretical imitation learning and practical, high-precision manipulation, making him a key figure to watch in the next generation of robotics research.