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About

Daeun Song is a robotics researcher whose work spans two compelling domains: artistic robotic systems and intelligent robot navigation. With a foundation in human-robot interaction and autonomous systems, Song has made significant contributions to both the creative and practical frontiers of robotics. Song's early work pioneered robotic pen-drawing systems capable of producing artistic strokes on arbitrary and nonplanar surfaces without explicit surface reconstruction, leveraging impedance-controlled 7DoF manipulators and conformal mapping techniques. These contributions — including the widely cited 2018 system (42 citations) and its distortion-free successor — represent a unique intersection of robotics, computer graphics, and artistic performance. More recently, Song has turned to socially aware robot navigation, developing visually grounded, language-model-driven approaches such as VLM-Social-Nav (36 citations) and the diffusion-based DTG trajectory generation framework, enabling robots to navigate complex, human-centered outdoor environments without reliance on pre-built maps. Across this body of work, Song has also contributed to legged locomotion planning through mixed-integer optimization frameworks (36 citations). Collectively accumulating nearly 190 citations, Song's research reflects a rare breadth — from robots that create art to robots that navigate society — making it highly relevant for students interested in embodied AI, human-robot coexistence, and autonomous manipulation.