Preference-Conditioned Language-Guided Abstraction

Andi Peng, Andreea Bobu, Belinda Z. Li, Theodore R. Sumers, Ilia Sucholutsky, Nishanth Kumar, Thomas L. Griffiths, Julie A. Shah

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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, these abstractions also depend on a user's preference for what matters in a task, which may be hard to describe or infeasible to exhaustively specify using language alone. How do we construct abstractions to capture these latent preferences? We observe that how humans behave reveals how they see the world. Our key insight is that changes in human behavior inform us that there are differences in preferences for how humans see the world, i.e. their state abstractions. In this work, we propose using language models (LMs) to query for those preferences directly given knowledge that a change in behavior has occurred. In our framework, we use the LM in two ways: first, given a text description of the task and knowledge of behavioral change between states, we query the LM for possible hidden preferences; second, given the most likely preference, we query the LM to construct the state abstraction. In this framework, the LM is also able to ask the human directly when uncertain about its own estimate. We demonstrate our framework's ability to construct effective preference-conditioned abstractions in simulated experiments, a user study, as well as on a real Spot robot performing mobile manipulation tasks.

Original languageEnglish (US)
Title of host publicationHRI 2024 - Proceedings of the 2024 ACM/IEEE International Conference on Human-Robot Interaction
PublisherIEEE Computer Society
Number of pages10
ISBN (Electronic)9798400703225
StatePublished - Mar 11 2024
Externally publishedYes
Event19th Annual ACM/IEEE International Conference on Human-Robot Interaction, HRI 2024 - Boulder, United States
Duration: Mar 11 2024Mar 15 2024

Publication series

NameACM/IEEE International Conference on Human-Robot Interaction
ISSN (Electronic)2167-2148


Conference19th Annual ACM/IEEE International Conference on Human-Robot Interaction, HRI 2024
Country/TerritoryUnited States

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Human-Computer Interaction
  • Electrical and Electronic Engineering


  • human preferences
  • learning from human input
  • state abstraction


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