Andrea Veronese
Papers
2
Total Citations
11
H-Index
2
About
Andrea Veronese is a researcher focused on advancing human-robot interaction through cognitive architectures and perceptual modeling. Her key research areas include human-aware robotics, attention mapping, and the development of memory-inspired artificial cognitive systems. Veronese’s major contribution lies in creating frameworks that enable robots to understand and respond to human physical, intentional, and emotional states, bridging the gap between machine perception and human-like cognition. Her most cited work, "Probabilistic Mapping of Human Visual Attention from Head Pose Estimation" (2017, 7 citations), introduces a method for robots to infer where a human is looking, enhancing bidirectional communication. In "Human-aware interaction: A memory-inspired artificial cognitive architecture" (2017, 4 citations), she proposes a system that integrates memory to support natural, context-aware interactions. Though her citation counts are modest, Veronese’s work is notable for its foundational approach to making robots more intuitive and empathetic partners. Her research is particularly relevant for students and researchers exploring socially assistive robotics, cognitive modeling, and the future of human-robot collaboration.
Research Focus
Key Achievements
Top Papers
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- 2