Zoe Falomir
Papers
11
Total Citations
103
H-Index
5
About
Zoe Falomir is a leading researcher in cognitive robotics, spatial cognition, and human-robot interaction, with a focus on bridging the gap between low-level sensor data and high-level symbolic reasoning. Her major contributions include developing qualitative image description models that enable robots to interpret and communicate about indoor scenes using spatial and visual features—work that has garnered over 100 citations across her most-cited papers. She pioneered the Probabilistic Reference And GRounding (PRAGR) mechanism, which allows robots to handle vague, human-like descriptions during dialogue, a key advancement for natural human-robot communication. Her 2012 thesis on qualitative distances and image descriptions for indoor scene recognition remains foundational, cited 16 times. Falomir also contributed to fuzzy sensor data integration for robust robot perception and qualitative shape description for manufacturing. She edited the special issue “Cognitive Robotics” in 2022, reflecting her leadership in the field. Her work is essential reading for students and researchers interested in making robots more perceptive, communicative, and cognitively aligned with humans.
Research Focus
Key Achievements
Top Papers
- 1
- 2Probabilistic reference and grounding with PRAGR for dialogues with robots21 citations · 2016
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- 5Customising a qualitative colour description for adaptability and usability10 citations · 2015
- 6Spatial Problem Solving and Cognition5 citations · 2017
- 7FUZZY DISTANCE SENSOR DATA INTEGRATION AND INTERPRETATION5 citations · 2011
- 8Special Issue “Cognitive Robotics”2 citations · 2022
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