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About

Andreas Seiderer is a leading researcher in human-robot interaction, specializing in socially assistive robotics for health and wellbeing. His work focuses on developing robots that can serve as personalized health companions, with key contributions in adaptive linguistic style and non-verbal communication. Seiderer pioneered methods for robots to adjust their language based on explicit human feedback, enabling more natural and effective interactions in assistive contexts. His most cited work, "Adaptive linguistic style for an assistive robotic health companion" (41 citations), demonstrates how robots can personalize their verbal communication to improve user engagement. He also advanced the synthesis of intentional and emotional non-verbal sounds for social robots (34 citations), allowing machines to express internal states without expert programming. Seiderer's "Drink-O-Mender" project (27 citations) explores robotic nutrition coaching, while his ongoing work on a robotic dietitian integrates natural language generation for persuasive health support. His research has significant implications for elderly care and independent living, with over 118 total citations across his publications. Seiderer's achievements include creating more empathetic and effective robotic health companions that can adapt to individual user needs.