Pallavi Koppol — accepted AI-conference papers

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 ↗

2021

Interaction Considerations in Learning from Humans

Pallavi Koppol, Henny Admoni, Reid Simmons

IJCAI 2021poster

The ability to learn from large quantities of complex data has led to the development of intelligent agents such as self-driving cars and assistive devices. This data often comes from people via interactions such as labeling, providing rewards and punishments, and giving demonstrations or critiques.…

Cited by 25 Source ↗PDF ↗Scholar ↗

2021

Understanding the Relationship between Interactions and Outcomes in Human-in-the-Loop Machine Learning

Yuchen Cui, Pallavi Koppol, Henny Admoni, Scott Niekum, Reid Simmons, Aaron Steinfeld +1

IJCAI 2021poster

Human-in-the-loop Machine Learning (HIL-ML) is a widely adopted paradigm for instilling human knowledge in autonomous agents. Many design choices influence the efficiency and effectiveness of such interactive learning processes, particularly the interaction type through which the human teacher may p…

Cited by 42 Source ↗PDF ↗Scholar ↗