Erin Hedlund-Botti — accepted AI-conference papers

2025

Learning Interpretable Features from Interventions

Erin Hedlund-Botti, Julianna Schalkwyk, Nina Marie Moorman, Chuxuan Yang, Lakshmi Seelam, Sanne Van Waveren +3

RSS 2025poster

The behavior of in-home robots must be adaptable to end-users to adequately address individual users’ needs and preferences. Learning from Demonstration (LfD) is a common approach for customizing robot behavior, enabling non-expert users to teach robots how to perform tasks according to their prefer…

Cited by 0 PDF ↗Scholar ↗

2024

Developing Design Guidelines for Older Adults with Robot Learning from Demonstration

Erin Hedlund-Botti, Lakshmi Seelam, Chuxuan Yang, Nathaniel Belles, Zulfiqar Haider Zaidi, Matthew Gombolay

RSS 2024poster

Assistive in-home robots have the potential to enable older adults to age in place by offloading mentally or physically demanding tasks to a robot. However, one challenge for in-home robots is that each individual will have differing needs, preferences, and home environments, which can all change ov…

Cited by 0 Source ↗PDF ↗Scholar ↗

2022

Reciprocal MIND MELD: Improving Learning From Demonstration via Personalized, Reciprocal Teaching

Mariah L Schrum, Erin Hedlund-Botti, Matthew Gombolay

CoRL 2022poster

Endowing robots with the ability to learn novel tasks via demonstrations will increase the accessibility of robots for non-expert, non-roboticists. However, research has shown that humans can be poor teachers, making it difficult for robots to effectively learn from humans. If the robot could instru…

Cited by 17 Source ↗Scholar ↗