Ravenna Thielstrom
Papers
8
Total Citations
109
H-Index
5
About
Ravenna Thielstrom is a leading researcher in human-robot interaction, specializing in expressive motion, team coordination, and transparent communication. Her work bridges robotics and behavioral science to create more intuitive, collaborative autonomous systems. Her most influential paper, "Expressive path shape (swagger)" (39 citations), demonstrates how simple variations in a robot’s movement can convey real-time attitudes toward its goals, drawing inspiration from acting training to make robot motion more readable and engaging. Thielstrom’s 2020 paper on "Genuine Robot Teammates" (30 citations) introduces a computational framework for shared mental models, enabling robots to coordinate effectively with humans by mimicking human teaming strategies. She also advances robot transparency through explanations and justifications (18 citations), ensuring robots can assess human instructions and respond appropriately. Her work on turn-entry timing for situated dialogue (7 citations) addresses a critical gap in conversational robotics, while her self-assessment dialogues (6 citations) empower robots to introspect on their capabilities before, during, and after missions. Thielstrom’s research consistently focuses on making robots more trustworthy, responsive, and effective partners in real-world tasks.
Research Focus
Key Achievements
Top Papers
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- 4It’s About Time: Turn-Entry Timing For Situated Human-Robot Dialogue7 citations · 2020
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