@inproceedings{liu-etal-2025-open-world,
title = "Open-World Planning via Lifted Regression with {LLM}-Inferred Affordances for Embodied Agents",
author = "Liu, Xiaotian and
Pesaranghader, Ali and
Li, Hanze and
Sukcharoenchaikul, Punyaphat and
Kim, Jaehong and
Sadhu, Tanmana and
Jeon, Hyejeong and
Sanner, Scott",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-long.1018/",
doi = "10.18653/v1/2025.acl-long.1018",
pages = "20881--20897",
ISBN = "979-8-89176-251-0",
abstract = "Open-world planning with incomplete knowledge is crucial for real-world embodied AI tasks. Despite that, existing LLM-based planners struggle with long chains of sequential reasoning, while symbolic planners face combinatorial explosion of states and actions for complex domains due to reliance on grounding. To address these deficiencies, we introduce LLM-Regress, an open-world planning approach integrating lifted regression with LLM-generated affordances. LLM-Regress generates sound and complete plans in a compact lifted form, avoiding exhaustive enumeration of irrelevant states and actions. Additionally, it makes efficient use of LLMs to infer goal-related objects and affordances without the need to predefine all possible objects and affordances. We conduct extensive experiments on three benchmarks and show that LLM-Regress significantly outperforms state-of-the-art LLM planners and a grounded planner using LLM-generated affordances."
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<abstract>Open-world planning with incomplete knowledge is crucial for real-world embodied AI tasks. Despite that, existing LLM-based planners struggle with long chains of sequential reasoning, while symbolic planners face combinatorial explosion of states and actions for complex domains due to reliance on grounding. To address these deficiencies, we introduce LLM-Regress, an open-world planning approach integrating lifted regression with LLM-generated affordances. LLM-Regress generates sound and complete plans in a compact lifted form, avoiding exhaustive enumeration of irrelevant states and actions. Additionally, it makes efficient use of LLMs to infer goal-related objects and affordances without the need to predefine all possible objects and affordances. We conduct extensive experiments on three benchmarks and show that LLM-Regress significantly outperforms state-of-the-art LLM planners and a grounded planner using LLM-generated affordances.</abstract>
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%0 Conference Proceedings
%T Open-World Planning via Lifted Regression with LLM-Inferred Affordances for Embodied Agents
%A Liu, Xiaotian
%A Pesaranghader, Ali
%A Li, Hanze
%A Sukcharoenchaikul, Punyaphat
%A Kim, Jaehong
%A Sadhu, Tanmana
%A Jeon, Hyejeong
%A Sanner, Scott
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F liu-etal-2025-open-world
%X Open-world planning with incomplete knowledge is crucial for real-world embodied AI tasks. Despite that, existing LLM-based planners struggle with long chains of sequential reasoning, while symbolic planners face combinatorial explosion of states and actions for complex domains due to reliance on grounding. To address these deficiencies, we introduce LLM-Regress, an open-world planning approach integrating lifted regression with LLM-generated affordances. LLM-Regress generates sound and complete plans in a compact lifted form, avoiding exhaustive enumeration of irrelevant states and actions. Additionally, it makes efficient use of LLMs to infer goal-related objects and affordances without the need to predefine all possible objects and affordances. We conduct extensive experiments on three benchmarks and show that LLM-Regress significantly outperforms state-of-the-art LLM planners and a grounded planner using LLM-generated affordances.
%R 10.18653/v1/2025.acl-long.1018
%U https://aclanthology.org/2025.acl-long.1018/
%U https://doi.org/10.18653/v1/2025.acl-long.1018
%P 20881-20897
Markdown (Informal)
[Open-World Planning via Lifted Regression with LLM-Inferred Affordances for Embodied Agents](https://aclanthology.org/2025.acl-long.1018/) (Liu et al., ACL 2025)
ACL
- Xiaotian Liu, Ali Pesaranghader, Hanze Li, Punyaphat Sukcharoenchaikul, Jaehong Kim, Tanmana Sadhu, Hyejeong Jeon, and Scott Sanner. 2025. Open-World Planning via Lifted Regression with LLM-Inferred Affordances for Embodied Agents. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 20881–20897, Vienna, Austria. Association for Computational Linguistics.