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About

Eleanor Avrunin’s research lies at the intersection of human-robot interaction, social robotics, and autonomous navigation, where she explores how robots can better understand and respond to human social cues. Her most influential work, “Robots that express emotion elicit better human teaching” (47 citations), demonstrates that robots capable of emotional expression—through speech or movement—can significantly improve human-robot collaboration, as participants were more engaged and effective when teaching a robot to dance. This finding has broad implications for designing robots that learn from people in natural settings. Avrunin also advanced social navigation, notably in “Socially-appropriate approach paths using human data” (13 citations), where she used human movement data to program robots to approach people in socially acceptable ways, moving beyond treating humans as mere obstacles. Her studies on robot dance and synchrony (18 citations) further reveal how low-level motion can create impressions of lifelikeness and agency, enhancing user engagement. With a total of over 90 citations across her top papers, Avrunin’s work is foundational for creating robots that are not only functional but also socially intuitive, paving the way for more natural and effective human-robot interactions in everyday environments.