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About

Tobias Baur is a leading researcher in socially interactive robotics and human-robot interaction (HRI), with a focus on creating adaptive, engaging, and socially-aware autonomous systems. His work centers on developing computational models that enable robots to understand and respond to human social cues, particularly through gaze behavior and linguistic style adaptation. Baur’s most influential contribution is his 2017 paper on adapting a robot’s linguistic style using socially-aware reinforcement learning (66 citations), which demonstrated that adaptive behavior significantly increases user engagement in long-term interactions. He also made foundational contributions to grounding in multimodal HRI, exploring how gaze mechanisms establish, maintain, and repair common ground between humans and robots (65 citations for his 2014 work). Beyond these core areas, Baur has explored novel applications such as multimodal joke generation for socially-aware robots and context-sensitive analysis of social interactions using cooperative machine learning. His research has been published in top venues including ACM/IEEE International Conference on Human-Robot Interaction and International Conference on Multimodal Interaction. With over 150 total citations, Baur’s work continues to shape how robots perceive and adapt to human social behavior, advancing the development of truly conversational companions.