Mathieu De Coster
Papers
1
Total Citations
4
H-Index
1
About
Mathieu De Coster is a researcher at the forefront of human-robot interaction, specializing in enabling social robots to perceive and respond to human social signals. His key research areas include multi-modal interaction, error detection, and failure prediction in collaborative robotics. De Coster’s major contribution lies in developing neural and traditional machine learning models that analyze temporal data from multiple modalities—such as speech, gesture, and gaze—to predict when a robot has made a mistake or when a user feels awkward. His work, notably presented in the 2024 paper "Predicting Errors and Failures in Human-Robot Interaction from Multi-Modal Temporal Data," has already garnered early citations, reflecting its timely relevance to the ERR@HRI challenge. By advancing robots’ ability to detect subtle cues of user discomfort or system failure, De Coster is paving the way for more intuitive, trustworthy, and socially aware robotic companions. His research holds significant promise for improving real-world human-robot collaboration in settings ranging from assistive care to industrial teamwork.
Research Focus
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Top Papers
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