Papers
24
Total Citations
189
H-Index
9
About
Daniel C. Tozadore is a researcher specializing in human-robot interaction (HRI), educational robotics, and adaptive social robotic systems. His work focuses on developing and evaluating robots as pedagogical tools, particularly for children, exploring how robotic behavior influences learning outcomes and user engagement. Tozadore's most influential contribution examines how operational conditions—specifically Wizard of Oz versus autonomous control—affect children's perception of robots (28 citations), offering critical methodological insights for the HRI community. His investigations into behavioral variation in humanoid robots, such as NAO, demonstrate how interactivity levels shape pedagogical effectiveness (17 citations), while his geometry-learning game highlights the unique intimacy robotic platforms can bring to educational settings (17 citations). His R-CASTLE project (15 citations) advances cognitive adaptive systems for robot-assisted teaching, pushing toward genuine personalization in learning environments. Beyond education, Tozadore has explored emotion recognition and embodied emotional expression in domestic robots (9 citations each), as well as UAV-based ground vehicle detection (13 citations), reflecting a broader technical versatility. His 2022 work on autonomous handwriting training robots (12 citations) underscores his commitment to accessible, technology-driven interventions for children with learning difficulties. Collectively, his research positions him as a thoughtful contributor to socially intelligent, child-centered robotics.
Research Focus
Key Achievements
Top Papers
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- 5Ground Vehicle Detection and Classification by an Unmanned Aerial Vehicle13 citations · 2015
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- 7Tablets and humanoid robots as engaging platforms for teaching languages12 citations · 2017
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- 9Effects of Emotion Grouping for Recognition in Human-Robot Interactions9 citations · 2018
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