Erin Botti
Papers
1
Total Citations
7
H-Index
1
About
Erin Botti is a rising researcher in human-robot interaction, with a focus on Learning from Demonstration (LfD)—a field that empowers non-expert users to teach robots complex behaviors. Her work addresses a critical challenge: enabling robots to solve long-horizon tasks by leveraging hierarchical task structures, a capability that remains elusive in current LfD systems. In her most-cited paper, "Investigating the Impact of Experience on a User's Ability to Perform Hierarchical Abstraction" (2023, 7 citations), Botti explores how a user's prior experience influences their capacity to decompose tasks into hierarchical abstractions, a key step for scalable robot learning. This research bridges the gap between human intuition and robotic autonomy, offering insights into designing more intuitive teaching interfaces. Though early in her career, Botti’s contributions are already shaping how robots interpret and execute complex, multi-step instructions from everyday users. Her work underscores the importance of human factors in robotics, paving the way for more accessible and capable autonomous systems. As she continues to investigate the interplay between user expertise and robot learning, Botti is poised to make lasting impacts on human-robot collaboration.
Research Focus
Key Achievements
Top Papers
- 1