mlsanityData to 14 Oct 2026

A Systematic Study of Large Language Models for Task and Motion Planning With PDDLStream

arXiv 2510.00182 ↗First posted 30 Sep 2025cs.RO cs.AIcited by 4

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Abstract (arXiv)

While we know that large language models (LLMs) can solve some planning problems, we do not understand the extent of these capabilities for robotics. One promising direction is to integrate the semantic knowledge of LLMs with the formal reasoning of task and motion planning (TAMP). However, designing such systems is complicated by the myriad of choices for how to integrate LLMs within TAMP. We develop 16 algorithms that use LLMs to substitute key TAMP components. Our zero-shot experiments across 13750 evaluations and three domains reveal that LLM-based planners exhibit lower success rates and higher planning times than engineered systems. Providing geometric details increases the number of task-planning errors compared to pure PDDL descriptions, and (faster) direct LLM variants outperform (slower) reasoning variants in most cases. Code and results are available at https://github.com/jorge-a-mendez/llm-pddlstream.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 27 of its 38 references that are in the map. First posted on arXiv 30 Sep 2025.

Builds on

27 of 38 references.

References · 27

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
  2. 2023Segment Anythingcited by 8,234
  3. 2023DINOv2: Learning Robust Visual Features without Supervisioncited by 7,974
  4. 2020Scaling Laws for Neural Language Modelscited by 6,586
  5. 2025Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilitiescited by 3,796
  6. 2024SAM 2: Segment Anything in Images and Videoscited by 2,863
  7. 2023Diffusion Policy: Visuomotor Policy Learning via Action Diffusioncited by 2,838
  8. 2024π0: A Vision-Language-Action Flow Model for General Robot Controlcited by 2,407
  9. 2022Do As I Can, Not As I Say: Grounding Language in Robotic Affordancescited by 2,348
  10. 2023PaLM-E: An Embodied Multimodal Language Modelcited by 2,071
  11. 2025π0.5: a Vision-Language-Action Model with Open-World Generalizationcited by 1,761
  12. 2022DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detectioncited by 1,319
15 more
  1. 2023LLM+P: Empowering Large Language Models with Optimal Planning Proficiencycited by 501
  2. 2020Integrated Task and Motion Planningcited by 290
  3. 2024LLMs Can't Plan, But Can Help Planning in LLM-Modulo Frameworkscited by 179
  4. 2022Open-vocabulary Queryable Scene Representations for Real World Planningcited by 160
  5. 2018PDDLStream: Integrating Symbolic Planners and Blackbox Samplers via Optimistic Adaptive Planningcited by 150
  6. 2025A Careful Examination of Large Behavior Models for Multitask Dexterous Manipulationcited by 129
  7. 2019Online Replanning in Belief Space for Partially Observable Task and Motion Problemscited by 61
  8. 2021Learning Symbolic Operators for Task and Motion Planningcited by 58
  9. 2022Learning Neuro-Symbolic Skills for Bilevel Planningcited by 58
  10. 2024Practice Makes Perfect: Planning to Learn Skill Parameter Policiescited by 23
  11. 2024Trust the PRoC3S: Solving Long-Horizon Robotics Problems with LLMs and Constraint Satisfactioncited by 19
  12. 2022Learning Efficient Abstract Planning Models that Choose What to Predictcited by 17
  13. 2024From Pixels to Predicates: Learning Symbolic World Models via Pretrained Vision-Language Modelscited by 11
  14. 2024Differentiable GPU-Parallelized Task and Motion Planningcited by 7
  15. 2025Meta-Optimization and Program Search using Language Models for Task and Motion Planningcited by 3

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 3.

4 papers.

  1. 2026Safe and Interpretable Multimodal Path Planning for Multi-Agent Cooperationcited by 3
  2. 2025Unifying Deep Predicate Invention with Pre-trained Foundation Modelscited by 2
  3. 2026Coding Agents for Generalized Task and Motion Planning Problemscited by 2
  4. 2026On the Generalization Gap in LLM Planning: Tests and Verifier-Reward RLcited by 1

Similar, not cited

Close in wording, no citation either way

  1. 2025Can LLM-Reasoning Models Replace Classical Planning? A Benchmark Study
  2. 2025One Demo Is All It Takes: Planning Domain Derivation with LLMs from A Single Demonstration
  3. 2024LLM3:Large Language Model-based Task and Motion Planning with Motion Failure Reasoning
  4. 2025Code-as-Symbolic-Planner: Foundation Model-Based Robot Planning via Symbolic Code Generation
  5. 2025T3 Planner: A Self-Correcting LLM Framework for Robotic Motion Planning with Temporal Logic
  6. 2023Translating Natural Language to Planning Goals with Large-Language Models
  7. 2025AuDeRe: Automated Strategy Decision and Realization in Robot Planning and Control via LLMs
  8. 2026GAVEL: Graph World Models for Verified and Efficient Long-Horizon LLM Task Planning