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About

Jouh Yeong Chew is a researcher at the forefront of social robotics and human-robot interaction, specializing in enabling robots to navigate and facilitate complex multi-party social settings. His work bridges machine vision and social intelligence, with early contributions in omnidirectional vision for mobile robot navigation (21 citations) laying the groundwork for his current focus. Chew’s major contributions center on developing frameworks that allow robots to understand and respond to human non-verbal cues—such as joint attention, gaze, and body language—to enhance group harmony and engagement. His 2023 paper on teaching robots to facilitate multi-party interactions (9 citations) and his 2024 work on joint attention estimation using multi-modal fusion (7 citations) are pivotal, proposing novel methods for robots to interpret social dynamics without requiring wearable sensors. More recently, Chew has advanced the field by modeling social interaction dynamics through temporal graph networks, offering a robust representation of how human behaviors and internal states mutually influence one another in group settings. With a growing citation impact and a clear trajectory from foundational vision systems to cutting-edge social AI, Chew’s research is shaping how intelligent agents can seamlessly integrate into human social environments, making him a key figure in the next generation of collaborative robotics.