@inproceedings{banerjee-gupta-2025-personalized,
title = "Personalized open world plan generation for safety-critical human centered autonomous systems: A case study on Artificial Pancreas",
author = "Banerjee, Ayan and
Gupta, Sandeep",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-emnlp.1219/",
doi = "10.18653/v1/2025.findings-emnlp.1219",
pages = "22409--22422",
ISBN = "979-8-89176-335-7",
abstract = "Design-time safety guarantees for human-centered autonomous systems (HCAS) often break down in open-world deployment due to uncertain human interaction. In practice, HCAS must follow a user-personalized safety plan, with the human providing external inputs to handle out-of-distribution events. Open-world safety planning for HCAS demands modeling dynamical systems, exploring novel actions, and rapid replanning when plans are invalidated or dynamics shift. No single state-of-the-art planner meets all these needs. We introduce an LLM-based architecture that automatically generates personalized safety plans. By itself, the LLM fares poorly at producing safe usage plans, but coupling it with a safety verifier{---}which evaluates plan safety over the planning horizon and feeds back quality scores{---}enables the discovery of safe plans. Moreover, fine-tuning the LLM on personalized models inferred from open-world data further enhances plan quality. We validate our approach by generating safe usage plans for artificial pancreas systems in automated insulin delivery for Type 1 Diabetes patients. Code: \url{https://github.com/ImpactLabASU/LLMOpen}"
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<abstract>Design-time safety guarantees for human-centered autonomous systems (HCAS) often break down in open-world deployment due to uncertain human interaction. In practice, HCAS must follow a user-personalized safety plan, with the human providing external inputs to handle out-of-distribution events. Open-world safety planning for HCAS demands modeling dynamical systems, exploring novel actions, and rapid replanning when plans are invalidated or dynamics shift. No single state-of-the-art planner meets all these needs. We introduce an LLM-based architecture that automatically generates personalized safety plans. By itself, the LLM fares poorly at producing safe usage plans, but coupling it with a safety verifier—which evaluates plan safety over the planning horizon and feeds back quality scores—enables the discovery of safe plans. Moreover, fine-tuning the LLM on personalized models inferred from open-world data further enhances plan quality. We validate our approach by generating safe usage plans for artificial pancreas systems in automated insulin delivery for Type 1 Diabetes patients. Code: https://github.com/ImpactLabASU/LLMOpen</abstract>
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%0 Conference Proceedings
%T Personalized open world plan generation for safety-critical human centered autonomous systems: A case study on Artificial Pancreas
%A Banerjee, Ayan
%A Gupta, Sandeep
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Findings of the Association for Computational Linguistics: EMNLP 2025
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-335-7
%F banerjee-gupta-2025-personalized
%X Design-time safety guarantees for human-centered autonomous systems (HCAS) often break down in open-world deployment due to uncertain human interaction. In practice, HCAS must follow a user-personalized safety plan, with the human providing external inputs to handle out-of-distribution events. Open-world safety planning for HCAS demands modeling dynamical systems, exploring novel actions, and rapid replanning when plans are invalidated or dynamics shift. No single state-of-the-art planner meets all these needs. We introduce an LLM-based architecture that automatically generates personalized safety plans. By itself, the LLM fares poorly at producing safe usage plans, but coupling it with a safety verifier—which evaluates plan safety over the planning horizon and feeds back quality scores—enables the discovery of safe plans. Moreover, fine-tuning the LLM on personalized models inferred from open-world data further enhances plan quality. We validate our approach by generating safe usage plans for artificial pancreas systems in automated insulin delivery for Type 1 Diabetes patients. Code: https://github.com/ImpactLabASU/LLMOpen
%R 10.18653/v1/2025.findings-emnlp.1219
%U https://aclanthology.org/2025.findings-emnlp.1219/
%U https://doi.org/10.18653/v1/2025.findings-emnlp.1219
%P 22409-22422
Markdown (Informal)
[Personalized open world plan generation for safety-critical human centered autonomous systems: A case study on Artificial Pancreas](https://aclanthology.org/2025.findings-emnlp.1219/) (Banerjee & Gupta, Findings 2025)
ACL