Michael Miksch

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About

Michael Miksch is a researcher at the forefront of human-robot interaction, with a particular focus on making robots more intuitive and responsive to human behavior. His key research areas include automatic error detection, non-verbal communication, and adaptive human-robot collaboration. Miksch’s most notable contribution is his pioneering work on using head and shoulder movements as a reliable signal for detecting when a robot has made an error during interaction. In his highly cited 2017 paper, "Head and Shoulders: Automatic Error Detection in Human-Robot Interaction" (32 citations), he introduced a novel classification method that allows robots to recognize user frustration or confusion through subtle body language cues. This work has significant implications for developing more socially aware robots that can self-correct in real-time. By shifting the focus from explicit verbal feedback to implicit behavioral signals, Miksch’s research helps bridge the gap between human expectations and robotic performance. His findings are widely referenced in the fields of social robotics and affective computing, and they continue to influence the design of safer, more cooperative robotic systems in both industrial and domestic settings.