mlsanityData to 7 Oct 2026

CLMASP: Coupling Large Language Models with Answer Set Programming for Robotic Task Planning

arXiv 2406.03367 ↗First posted 5 Jun 2024cs.AIcited by 3

On the map: LLM reasoning → Spatial and embodied reasoning

Abstract (arXiv)

Large Language Models (LLMs) possess extensive foundational knowledge and moderate reasoning abilities, making them suitable for general task planning in open-world scenarios. However, it is challenging to ground a LLM-generated plan to be executable for the specified robot with certain restrictions. This paper introduces CLMASP, an approach that couples LLMs with Answer Set Programming (ASP) to overcome the limitations, where ASP is a non-monotonic logic programming formalism renowned for its capacity to represent and reason about a robot's action knowledge. CLMASP initiates with a LLM generating a basic skeleton plan, which is subsequently tailored to the specific scenario using a vector database. This plan is then refined by an ASP program with a robot's action knowledge, which integrates implementation details into the skeleton, grounding the LLM's abstract outputs in practical robot contexts. Our experiments conducted on the VirtualHome platform demonstrate CLMASP's efficacy. Compared to the baseline executable rate of under 2% with LLM approaches, CLMASP significantly improves this to over 90%.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Placed in Spatial and embodied reasoning, one of 17 directions in LLM reasoning. Builds on 17 of its 49 references that are in the map. First posted on arXiv 5 Jun 2024.

Builds on

17 of 49 references.

Closest ancestors · 1

Same direction, newest first

  1. 2023EmbodiedGPT: Vision-Language Pre-Training via Embodied Chain of Thought24 May

Other references · 16

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2022Code as Policies: Language Model Programs for Embodied Controlcited by 1,172
  3. 2022Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agentscited by 1,029
  4. 2023CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquingcited by 751
  5. 2023LLM+P: Empowering Large Language Models with Optimal Planning Proficiencycited by 496
  6. 2022LLM-Planner: Few-Shot Grounded Planning for Embodied Agents with Large Language Modelscited by 459
  7. 2023Logic-LM: Empowering Large Language Models with Symbolic Solvers for Faithful Logical Reasoningcited by 379
  8. 2023Language Models Meet World Models: Embodied Experiences Enhance Language Modelscited by 123
  9. 2017Multi-shot ASP solving with clingocited by 115
  10. 2023AutoTAMP: Autoregressive Task and Motion Planning with LLMs as Translators and Checkerscited by 111
  11. 2023Embodied Task Planning with Large Language Modelscited by 84
  12. 2023Coupling Large Language Models with Logic Programming for Robust and General Reasoning from Textcited by 59
4 more
  1. 2024Grounding LLMs For Robot Task Planning Using Closed-loop State Feedbackcited by 25
  2. 2022Answer Set Planning: A Surveycited by 12
  3. 2020Omission-based Abstraction for Answer Set Programscited by 7
  4. 2023A Unified View on Forgetting and Strong Equivalence Notions in Answer Set Programmingcited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 2; 2026: 1.

3 papers.

  1. 2025PDE-Controller: LLMs for Autoformalization and Reasoning of PDEscited by 23
  2. 2025NeSyPr: Neurosymbolic Proceduralization For Efficient Embodied Reasoningcited by 5
  3. 2026LLMs as ASP Programmers: Self-Correction Enables Task-Agnostic Nonmonotonic Reasoningcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025A Systematic Study of Large Language Models for Task and Motion Planning With PDDLStream
  2. 2025Combining Large Language Models and Symbolic Reasoning for Multi-Robot Temporal Planning through Explainable Knowledge Bases
  3. 2025Can LLM-Reasoning Models Replace Classical Planning? A Benchmark Study
  4. 2025Code-as-Symbolic-Planner: Foundation Model-Based Robot Planning via Symbolic Code Generation
  5. 2025AuDeRe: Automated Strategy Decision and Realization in Robot Planning and Control via LLMs
  6. 2024Nl2Hltl2Plan: Scaling Up Natural Language Understanding for Multi-Robots Through Hierarchical Temporal Logic Task Representation
  7. 2026GAVEL: Graph World Models for Verified and Efficient Long-Horizon LLM Task Planning
  8. 2024Can only LLMs do Reasoning?: Potential of Small Language Models in Task Planning