Erin Hedlund-Botti
Papers
7
Total Citations
107
H-Index
4
About
Erin Hedlund-Botti is a human-robot interaction (HRI) researcher whose work spans robot learning, collaborative robotics, and research methodology, with a particular focus on making robots more adaptable and accessible to everyday users. Her most-cited contribution, "Concerning Trends in Likert Scale Usage in Human-Robot Interaction" (2022, 46 citations), demonstrates her commitment to methodological rigor within the HRI community, advocating for stronger statistical practices across the field. Equally significant is her work on MIND MELD (2022, 23 citations), a personalized meta-learning framework for robot-centric imitation learning that addresses the longstanding challenge of enabling non-expert users to effectively teach robots new tasks — a pressing concern as assistive robots increasingly enter home environments. Her research on proximate human-robot collaboration (2023, 16 citations) and the impacts of in-situ robot learning on user attitudes (2023, 11 citations) further illustrate her dedication to designing robots that are both safe and socially intelligent. Hedlund-Botti's broader portfolio, encompassing teleoperation performance and learning from imperfect demonstrations, reflects a cohesive vision: building robotic systems that meaningfully and reliably serve aging and caregiving populations in the real world.
Research Focus
Key Achievements
Top Papers
- 1
- 2MIND MELD: Personalized Meta-Learning for Robot-Centric Imitation Learning23 citations · 2022
- 3
- 4Impacts of Robot Learning on User Attitude and Behavior11 citations · 2023
- 5
- 6
- 7