Fereshta Yazdani
Papers
8
Total Citations
69
H-Index
5
About
Fereshta Yazdani’s research lies at the vital intersection of human-robot interaction, cognitive robotics, and natural language understanding, with a focused application in mixed human-robot rescue teams. Her major contributions center on developing cognition-enabled frameworks that allow robots to resolve referential ambiguity and interpret task-based natural language commands in dynamic, high-stakes environments. Notably, her work on the Dempster-Shafer theoretic resolution of referential ambiguity (18 citations) provides a robust mathematical approach for robots to handle uncertain human instructions. Yazdani has also pioneered methods for mixed teams to acquire knowledge of object arrangements from human examples, enabling household and rescue robots to better understand their surroundings. Her most cited papers, including those on cognition-enabled robot control and guidelines for improving natural language understanding in rescue teams, collectively demonstrate her impact on making robotic teammates more intuitive and effective. Through her involvement in the SHERPA project, she has advanced the integration of heterogeneous knowledge bases for outdoor search and rescue, showcasing her commitment to real-world deployment. With over 69 citations across her key works, Yazdani’s research is shaping the future of collaborative human-robot teams in critical missions.
Research Focus
Key Achievements
Top Papers
- 1Dempster-Shafer theoretic resolution of referential ambiguity18 citations · 2018
- 2Cognition-enabled Framework for Mixed Human-Robot Rescue Teams18 citations · 2018
- 3Cognition-Enabled Robot Control for Mixed Human-Robot Rescue Teams11 citations · 2015
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