Patrick Callaghan — accepted AI-conference papers

2025

Optimal Interactive Learning on the Job via Facility Location Planning

Shivam Vats, Michelle D. Zhao, Patrick Callaghan, Mingxi Jia, Maxim Likhachev, Oliver Kroemer +1

RSS 2025poster

Collaborative robots have the ability to adapt and improve their behavior by learning from their human users. By interactively learning on the job, these robots can both acquire new motor skills and customize their behavior to personal user preferences. However, for this paradigm to be viable, there…

Cited by 0 PDF ↗Scholar ↗

2022

INQUIRE: INteractive Querying for User-aware Informative REasoning

Tesca Fitzgerald, Pallavi Koppol, Patrick Callaghan, Russell Quinlan Jun Hei Wong, Reid Simmons, Oliver Kroemer +1

CoRL 2022poster

Research on Interactive Robot Learning has yielded several modalities for querying a human for training data, including demonstrations, preferences, and corrections. While prior work in this space has focused on optimizing the robot's queries within each interaction type, there has been little work…

Cited by 24 Source ↗Scholar ↗