Manizheh Zand
Papers
9
Total Citations
35
H-Index
4
About
Manizheh Zand is a rising researcher in human-robot interaction (HRI), whose work centers on making robots more accessible and intuitive through natural spoken language. Her primary contributions lie in developing frameworks that allow robots to automatically generate action sequences from unstructured speech, enabling seamless collaboration without requiring users to follow rigid scripts. In her 2023 paper “Towards Robot Learning from Spoken Language” (8 citations), she demonstrated a system capable of distinguishing task-relevant commands from casual conversation—a critical step toward robots that can operate in real-world social environments. Zand also addresses the human side of HRI: her 2024 study on gender dynamics in trust, privacy, and safety perceptions (5 citations) synthesizes psychology, ethics, and technology to reveal how diverse users experience robot interactions differently. Additionally, her work on assistive robots for persons with visual impairments (5 citations) identifies open challenges in designing robots that truly support users with blindness. With recent investigations into cognitive fatigue assessment during daily living tasks, Zand is pushing toward robots that not only understand speech but also monitor user well-being. Her growing body of work—already accumulating over 30 citations—positions her as a thoughtful voice shaping the future of inclusive, speech-driven human-robot collaboration.
Research Focus
Key Achievements
Top Papers
- 1Towards Robot Learning from Spoken Language8 citations · 2023
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