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About

Junyun Tay’s research lies at the intersection of robotics, human-robot interaction, and autonomous behavior, with a particular focus on enabling robots to move expressively and intelligently. His most cited work, “Autonomous robot dancing driven by beats and emotions of music” (45 citations), pioneered methods for robots to choreograph their own dance movements in real time—freeing them from preprogrammed routines and allowing synchronization with musical rhythm and emotional cues. Tay also made foundational contributions to multi-robot coordination in the RoboCup Standard Platform League, where his modeling of world states for NAO humanoid soccer robots (22 citations) helped advance fully autonomous team play. In human-robot interaction, he formalized gesture representation and synchronization with speech (11 citations), enabling more natural non-verbal communication. His work on fall prediction for novel motion sequences (9 citations) and autonomous motion-to-label mapping (2 citations) further demonstrates his drive to make robots more adaptive and self-sufficient. Tay’s research has been instrumental in moving robot motion from rigid, human-designed scripts toward fluid, context-aware autonomy—a key step for robots that must dance, play, or converse alongside people.