Preference-Conditioned Language-Guided Abstraction

Learning from demonstrations is a common way for users to teach robots, but it is prone to spurious feature correlations. Recent work constructs state abstractions, i.e. visual representations containing task-relevant features, from language as a way to perform more generalizable learning. However,...

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Bibliographic Details

Published in:	2024 19th ACM/IEEE International Conference on Human-Robot Interaction (HRI) pp. 572 - 581
Main Authors:	Peng, Andi, Bobu, Andreea, Li, Belinda Z., Sumers, Theodore R., Sucholutsky, Ilia, Kumar, Nishanth, Griffiths, Thomas L., Shah, Julie A.
Format:	Conference Proceeding
Language:	English
Published:	ACM 11.03.2024
Subjects:	Behavioral sciences Correlation human preferences Human-robot interaction learning from human input Robots state abstraction Visualization
Online Access:	Get full text
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