Papers
17
Total Citations
336
H-Index
8
About
Yudith Cardinale is a prominent researcher whose work sits at the intersection of social robotics, artificial intelligence, and knowledge representation. Her research primarily focuses on Human-Robot Interaction (HRI), emotion detection, autonomous navigation, and ontology-driven systems for intelligent robots. Cardinale has made significant contributions to the development of multimodal emotion recognition frameworks, most notably her adaptive architecture for social robots (2022, 88 citations) and her NLP transformer-based emotion detection system grounded in emotion ontologies (2021, 83 citations), which together have reshaped how robots perceive and respond to human affective states. Her work on proxemics-based social navigation in crowded environments (2021, 54 citations) demonstrates a keen understanding of how robots must operate safely and naturally alongside humans. Beyond emotion and navigation, Cardinale has advanced the field of knowledge representation in robotics through ontologies for Simultaneous Localization and Mapping (SLAM), with both survey and applied contributions. More recently, she has explored heterogeneous multi-robot middleware systems and robot emotion expression evaluation, underscoring her commitment to building holistic, socially intelligent robotic systems. With over 300 cumulative citations, her work continues to meaningfully shape the future of socially aware autonomous robotics.
Research Focus
Key Achievements
Top Papers
- 1Adaptive Multimodal Emotion Detection Architecture for Social Robots88 citations · 2022
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- 4Group Emotion Detection Based on Social Robot Perception30 citations · 2022
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- 7Evaluation of Robot Emotion Expressions for Human–Robot Interaction10 citations · 2024
- 8IoRT-Based Middleware for Heterogeneous Multi-Robot Systems9 citations · 2024
- 9Application of a methodological approach to compare ontologies6 citations · 2021
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