Sebastian Loth

Papers

Total Citations

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About

Sebastian Loth investigates the intersection of social robotics and human-robot interaction, with a particular focus on how machines can interpret and respond to human social signals in service settings. His most significant contribution is the development of the "Ghost-in-the-Machine" (GiM) paradigm, a novel experimental method that allows researchers to study how humans recognize social intentions by having a human operator secretly control a robot's actions. This approach, detailed in his 2015 paper (27 citations), revealed critical insights into the subtle cues customers use when initiating interactions with a robotic bartender. Loth's work on automatic detection of service initiation signals (2013, 23 citations) further advanced the field by identifying which sensor modalities—such as gaze, posture, or gestures—are most informative for enabling robots to recognize when a customer wants service. His research directly addresses the challenge of designing robots that can navigate complex social dynamics, making him a key figure in developing more intuitive and responsive robotic systems for real-world service environments.