@inproceedings{xu-etal-2026-tess,
title = "{TESS}: {LLM}-guided Translation, Exploration, Simulation, and Search for Embodied Task Planning",
author = "Xu, Peiran and
Yang, Yixiang and
Li, Jinghan and
Li, Fei and
Sheng, Cao and
Yu, JiaZhong and
MU, Yadong",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.642/",
pages = "13457--13487",
ISBN = "979-8-89176-462-0",
abstract = "In this work we focus on the problem of embodied task planning, which requires an agent to accomplish a complex task by separating it into atomic actions. Recent studies have developed LLM-based methods to address this problem. However, the executability and correctness of the generated plans remain suboptimal in complex environments and tasks. On the other hand, classical search algorithms have the potential to solve the planning problems perfectly, yet they require fully observable environments, known state transitions and goal state, which are often unattainable in general task domains. Therefore, we propose to implement an LLM-guided search algorithm that makes the search process possible and efficient by incorporating LLMs' commonsense knowledge. Our method leverages LLMs to (1) translate natural language instructions into formatted goal conditions, (2) facilitate efficient exploration in partially observable environments, (3) implement a rule-based world simulator, and (4) prune the space of candidate actions to improve search efficiency. Experimental results on VirtualHome and ALFWorld demonstrate the superior performance of our agent."
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%0 Conference Proceedings
%T TESS: LLM-guided Translation, Exploration, Simulation, and Search for Embodied Task Planning
%A Xu, Peiran
%A Yang, Yixiang
%A Li, Jinghan
%A Li, Fei
%A Sheng, Cao
%A Yu, JiaZhong
%A MU, Yadong
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F xu-etal-2026-tess
%X In this work we focus on the problem of embodied task planning, which requires an agent to accomplish a complex task by separating it into atomic actions. Recent studies have developed LLM-based methods to address this problem. However, the executability and correctness of the generated plans remain suboptimal in complex environments and tasks. On the other hand, classical search algorithms have the potential to solve the planning problems perfectly, yet they require fully observable environments, known state transitions and goal state, which are often unattainable in general task domains. Therefore, we propose to implement an LLM-guided search algorithm that makes the search process possible and efficient by incorporating LLMs’ commonsense knowledge. Our method leverages LLMs to (1) translate natural language instructions into formatted goal conditions, (2) facilitate efficient exploration in partially observable environments, (3) implement a rule-based world simulator, and (4) prune the space of candidate actions to improve search efficiency. Experimental results on VirtualHome and ALFWorld demonstrate the superior performance of our agent.
%U https://aclanthology.org/2026.findings-emnlp.642/
%P 13457-13487
Markdown (Informal)
[TESS: LLM-guided Translation, Exploration, Simulation, and Search for Embodied Task Planning](https://aclanthology.org/2026.findings-emnlp.642/) (Xu et al., Findings 2026)
ACL
- Peiran Xu, Yixiang Yang, Jinghan Li, Fei Li, Cao Sheng, JiaZhong Yu, and Yadong MU. 2026. TESS: LLM-guided Translation, Exploration, Simulation, and Search for Embodied Task Planning. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 13457–13487, Budapest, Hungary. Association for Computational Linguistics.