Jiong Sun

Papers

Total Citations

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About

Jiong Sun is a researcher at the forefront of affective human-robot interaction, specializing in how machines can perceive and respond to human social cues through touch and gesture. Their work bridges robotics, machine learning, and human communication, focusing on two key areas: classifying social gestures and interpreting tactile interactions. Sun’s most cited paper (2021, 7 citations) introduces a kinematic-based classification system that uses machine learning to distinguish social gestures from grasping movements, a critical step for enabling robots to engage in natural, non-verbal social exchanges with humans. In parallel, Sun’s foundational studies on social touch (2017, 3 and 2 citations) demonstrate how soft tactile sensors—such as matrix arrays—can capture nuanced human touch patterns. By applying six machine learning methods to this tactile data, Sun showed that touch type is reliably informative for communicated emotion, laying groundwork for robots that can “feel” and respond to affective contact. Though early in their career, Sun’s work is already shaping the design of empathetic, socially aware robots, making human-robot interaction more intuitive and emotionally resonant.