APRICOT: Active Preference Learning and Constraint-Aware Task Planning with LLMs

Huaxiaoyue Wang, Nathaniel Chin, Gonzalo Gonzalez-Pumariega, Xiangwan Sun, Neha Sunkara, Maximus Adrian Pace, Jeannette Bohg, Sanjiban Choudhury
Proceedings of The 8th Conference on Robot Learning, PMLR 270:1590-1642, 2025.

Abstract

Home robots performing personalized tasks must adeptly balance user preferences with environmental affordances. We focus on organization tasks within constrained spaces, such as arranging items into a refrigerator, where preferences for placement collide with physical limitations. The robot must infer user preferences based on a small set of demonstrations, which is easier for users to provide than extensively defining all their requirements. While recent works use Large Language Models (LLMs) to learn preferences from user demonstrations, they encounter two fundamental challenges. First, there is inherent ambiguity in interpreting user actions, as multiple preferences can often explain a single observed behavior. Second, not all user preferences are practically feasible due to geometric constraints in the environment. To address these challenges, we introduce APRICOT, a novel approach that merges LLM-based Bayesian active preference learning with constraint-aware task planning. APRICOT refines its generated preferences by actively querying the user and dynamically adapts its plan to respect environmental constraints. We evaluate APRICOT on a dataset of diverse organization tasks and demonstrate its effectiveness in real-world scenarios, showing significant improvements in both preference satisfaction and plan feasibility.

Cite this Paper

BibTeX
@InProceedings{pmlr-v270-wang25e, title = {APRICOT: Active Preference Learning and Constraint-Aware Task Planning with LLMs}, author = {Wang, Huaxiaoyue and Chin, Nathaniel and Gonzalez-Pumariega, Gonzalo and Sun, Xiangwan and Sunkara, Neha and Pace, Maximus Adrian and Bohg, Jeannette and Choudhury, Sanjiban}, booktitle = {Proceedings of The 8th Conference on Robot Learning}, pages = {1590--1642}, year = {2025}, editor = {Agrawal, Pulkit and Kroemer, Oliver and Burgard, Wolfram}, volume = {270}, series = {Proceedings of Machine Learning Research}, month = {06--09 Nov}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v270/main/assets/wang25e/wang25e.pdf}, url = {https://proceedings.mlr.press/v270/wang25e.html}, abstract = {Home robots performing personalized tasks must adeptly balance user preferences with environmental affordances. We focus on organization tasks within constrained spaces, such as arranging items into a refrigerator, where preferences for placement collide with physical limitations. The robot must infer user preferences based on a small set of demonstrations, which is easier for users to provide than extensively defining all their requirements. While recent works use Large Language Models (LLMs) to learn preferences from user demonstrations, they encounter two fundamental challenges. First, there is inherent ambiguity in interpreting user actions, as multiple preferences can often explain a single observed behavior. Second, not all user preferences are practically feasible due to geometric constraints in the environment. To address these challenges, we introduce APRICOT, a novel approach that merges LLM-based Bayesian active preference learning with constraint-aware task planning. APRICOT refines its generated preferences by actively querying the user and dynamically adapts its plan to respect environmental constraints. We evaluate APRICOT on a dataset of diverse organization tasks and demonstrate its effectiveness in real-world scenarios, showing significant improvements in both preference satisfaction and plan feasibility.} }
Endnote
%0 Conference Paper %T APRICOT: Active Preference Learning and Constraint-Aware Task Planning with LLMs %A Huaxiaoyue Wang %A Nathaniel Chin %A Gonzalo Gonzalez-Pumariega %A Xiangwan Sun %A Neha Sunkara %A Maximus Adrian Pace %A Jeannette Bohg %A Sanjiban Choudhury %B Proceedings of The 8th Conference on Robot Learning %C Proceedings of Machine Learning Research %D 2025 %E Pulkit Agrawal %E Oliver Kroemer %E Wolfram Burgard %F pmlr-v270-wang25e %I PMLR %P 1590--1642 %U https://proceedings.mlr.press/v270/wang25e.html %V 270 %X Home robots performing personalized tasks must adeptly balance user preferences with environmental affordances. We focus on organization tasks within constrained spaces, such as arranging items into a refrigerator, where preferences for placement collide with physical limitations. The robot must infer user preferences based on a small set of demonstrations, which is easier for users to provide than extensively defining all their requirements. While recent works use Large Language Models (LLMs) to learn preferences from user demonstrations, they encounter two fundamental challenges. First, there is inherent ambiguity in interpreting user actions, as multiple preferences can often explain a single observed behavior. Second, not all user preferences are practically feasible due to geometric constraints in the environment. To address these challenges, we introduce APRICOT, a novel approach that merges LLM-based Bayesian active preference learning with constraint-aware task planning. APRICOT refines its generated preferences by actively querying the user and dynamically adapts its plan to respect environmental constraints. We evaluate APRICOT on a dataset of diverse organization tasks and demonstrate its effectiveness in real-world scenarios, showing significant improvements in both preference satisfaction and plan feasibility.
APA
Wang, H., Chin, N., Gonzalez-Pumariega, G., Sun, X., Sunkara, N., Pace, M.A., Bohg, J. & Choudhury, S.. (2025). APRICOT: Active Preference Learning and Constraint-Aware Task Planning with LLMs. Proceedings of The 8th Conference on Robot Learning, in Proceedings of Machine Learning Research 270:1590-1642 Available from https://proceedings.mlr.press/v270/wang25e.html.

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