Kelvin Sheng Pei Dong

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About

Kelvin Sheng Pei Dong is a researcher in robotics and human-robot collaboration, with a focus on task planning and autonomous manipulation. His work centers on developing frameworks that enable robots to reason about and execute complex, real-world tasks—such as cooking—by accounting for both functional object relationships and the robot’s own physical capabilities. Dong’s most cited paper, “Task Planning with a Weighted Functional Object-Oriented Network” (2021, 15 citations), introduces a novel approach that weights object interactions to improve planning efficiency in shared human-robot environments. This builds on his earlier work, “Functional Object-Oriented Network: Considering Robot’s Capability in Human-Robot Collaboration” (2019, 3 citations), which laid the groundwork for integrating robot limitations into task models. Dong’s research addresses a critical gap in autonomy: while robots can perform many manipulation actions, risky or delicate steps still require human intervention. By explicitly modeling these trade-offs, his contributions help bridge the divide between full automation and practical, safe collaboration. His work is particularly relevant for researchers in assistive robotics, manufacturing, and domestic service robots, offering a principled way to design systems that adapt to both human and robot strengths.