Lakshmi Seelam — 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…

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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…

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2023

Investigating the Impact of Experience on a User's Ability to Perform Hierarchical Abstraction

Nina M Moorman, Nakul Gopalan, Aman Singh, Erin Botti, Mariah Schrum, Chuxuan Yang +2

RSS 2023poster

The field of Learning from Demonstration enables end-users, who are not robotics experts, to shape robot behavior. However, using human demonstrations to teach robots to solve long-horizon problems by leveraging the hierarchical structure of the task is still an unsolved problem. Prior work has yet…

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