Emmanuel Johnson

University of Southern California, North Carolina Agricultural and Technical State University

Papers

Total Citations

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About

Emmanuel Johnson investigates the intersection of human-robot interaction, social dialogue, and trust, with a particular focus on how conversational errors and rapport-building shape human perceptions of robotic agents. His most influential work, "Getting to Know Each Other" (2018, 57 citations), demonstrates that social dialogue can either mitigate or exacerbate trust loss when robots make conversational mistakes, using a NAO robot programmed to persuade users in ranking tasks. This research reveals the nuanced dynamics of human-robot relationships, showing that rapport-building is a double-edged sword—capable of both strengthening and undermining trust depending on context. Johnson also contributed to foundational tools for the field, including the "Niki and Julie Corpus" (2018, 4 citations), a collaborative multimodal dialogue dataset featuring interactions between humans, robots, and virtual agents. This corpus, alongside the companion paper "Niki and Julie: a robot and virtual human for studying multimodal social interaction" (2016, 4 citations), provides a valuable resource for studying social influence and familiarity-building in mixed-agent scenarios. Earlier work on robot localization using overhead cameras and LEDs (2012, 6 citations) reflects his foundational expertise in multi-agent systems. Johnson’s research offers critical insights for designing socially adept robots capable of navigating the complexities of human communication.