mlsanityData to 14 Oct 2026

AutoTAMP: Autoregressive Task and Motion Planning with LLMs as Translators and Checkers

arXiv 2306.06531 ↗First posted 10 Jun 2023cs.RO cs.CL cs.HCcited by 113

Not on the map. Why?

Cited by 21 in LLM agents and tool use, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

For effective human-robot interaction, robots need to understand, plan, and execute complex, long-horizon tasks described by natural language. Recent advances in large language models (LLMs) have shown promise for translating natural language into robot action sequences for complex tasks. However, existing approaches either translate the natural language directly into robot trajectories or factor the inference process by decomposing language into task sub-goals and relying on a motion planner to execute each sub-goal. When complex environmental and temporal constraints are involved, inference over planning tasks must be performed jointly with motion plans using traditional task-and-motion planning (TAMP) algorithms, making factorization into subgoals untenable. Rather than using LLMs to directly plan task sub-goals, we instead perform few-shot translation from natural language task descriptions to an intermediate task representation that can then be consumed by a TAMP algorithm to jointly solve the task and motion plan. To improve translation, we automatically detect and correct both syntactic and semantic errors via autoregressive re-prompting, resulting in significant improvements in task completion. We show that our approach outperforms several methods using LLMs as planners in complex task domains. See our project website https://yongchao98.github.io/MIT-REALM-AutoTAMP/ for prompts, videos, and code.

Key facts. Cited by 113 papers in the map (data through 14 Oct 2026). Builds on 26 of its 67 references that are in the map. First posted on arXiv 10 Jun 2023.

Builds on

26 of 67 references.

References · 26

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2022Large Language Models are Zero-Shot Reasonerscited by 4,374
  3. 2022Do As I Can, Not As I Say: Grounding Language in Robotic Affordancescited by 2,348
  4. 2022Code as Policies: Language Model Programs for Embodied Controlcited by 1,210
  5. 2022Inner Monologue: Embodied Reasoning through Planning with Language Modelscited by 1,081
  6. 2022Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agentscited by 1,035
  7. 2022ProgPrompt: Generating Situated Robot Task Plans using Large Language Modelscited by 611
  8. 2023LLM+P: Empowering Large Language Models with Optimal Planning Proficiencycited by 501
  9. 2020Integrated Task and Motion Planningcited by 290
  10. 2023Text2Motion: From Natural Language Instructions to Feasible Planscited by 224
  11. 2023TidyBot: Personalized Robot Assistance with Large Language Modelscited by 221
  12. 2023Leveraging Pre-trained Large Language Models to Construct and Utilize World Models for Model-based Task Planningcited by 207
14 more
  1. 2018PDDLStream: Integrating Symbolic Planners and Blackbox Samplers via Optimistic Adaptive Planningcited by 150
  2. 2023Task and Motion Planning with Large Language Models for Object Rearrangementcited by 141
  3. 2023Generalized Planning in PDDL Domains with Pretrained Large Language Modelscited by 139
  4. 2023Translating Natural Language to Planning Goals with Large-Language Modelscited by 133
  5. 2023NL2TL: Transforming Natural Languages to Temporal Logics using Large Language Modelscited by 59
  6. 2023ChatGPT Empowered Long-Step Robot Control in Various Environments: A Case Applicationcited by 48
  7. 2023Errors are Useful Prompts: Instruction Guided Task Programming with Verifier-Assisted Iterative Promptingcited by 46
  8. 2023Data-Efficient Learning of Natural Language to Linear Temporal Logic Translators for Robot Task Specificationcited by 38
  9. 2016FFRob: Leveraging Symbolic Planning for Efficient Task and Motion Planningcited by 34
  10. 2021DeepSTL -- From English Requirements to Signal Temporal Logiccited by 22
  11. 2016Towards Blended Reactive Planning and Acting using Behavior Treescited by 21
  12. 2022Multi-agent Motion Planning from Signal Temporal Logic Specificationscited by 21
  13. 2017Combined Task and Motion Planning as Classical AI Planningcited by 4
  14. 2022Interactive Learning from Natural Language and Demonstrations using Signal Temporal Logiccited by 2

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 9; 2024: 34; 2025: 46; 2026: 24.

113 papers.

Sort:

By area:

  1. 2023RoCo: Dialectic Multi-Robot Collaboration with Large Language Modelscited by 172
  2. 2023Look Before You Leap: Unveiling the Power of GPT-4V in Robotic Vision-Language Planningcited by 161
  3. 2026CaP-X: A Framework for Benchmarking and Improving Coding Agents for Robot Manipulationcited by 88
  4. 2025PlanGenLLMs: A Modern Survey of LLM Planning Capabilitiescited by 83
  5. 2025ReMA: Learning to Meta-think for LLMs with Multi-Agent Reinforcement Learningcited by 79
  6. 2024LLM3:Large Language Model-based Task and Motion Planning with Motion Failure Reasoningcited by 63
  7. 2023Dynamic Planning with a LLMcited by 61
  8. 2023Can Large Language Models be Good Path Planners? A Benchmark and Investigation on Spatial-temporal Reasoningcited by 49
  9. 2024Large Language Models Can Solve Real-World Planning Rigorously with Formal Verification Toolscited by 47
  10. 2024Guiding Long-Horizon Task and Motion Planning with Vision Language Modelscited by 47
  11. 2026Harness VLA: Steering Frozen VLAs into Reliable Manipulation Primitives via Memory-Guided Agentscited by 47
  12. 2024LLM-A*: Large Language Model Enhanced Incremental Heuristic Search on Path Planningcited by 45
38 more
  1. 2023Creative Robot Tool Use with Large Language Modelscited by 43
  2. 2024BadRobot: Jailbreaking Embodied LLM Agents in the Physical Worldcited by 43
  3. 2024On the Prospects of Incorporating Large Language Models (LLMs) in Automated Planning and Scheduling (APS)cited by 40
  4. 2024CRAB: Cross-environment Agent Benchmark for Multimodal Language Model Agentscited by 39
  5. 2025LLMs as Planning Formalizers: A Survey for Leveraging Large Language Models to Construct Automated Planning Modelscited by 38
  6. 2024Planning Anything with Rigor: General-Purpose Zero-Shot Planning with LLM-based Formalized Programmingcited by 35
  7. 2025Safety Guardrails for LLM-Enabled Robotscited by 35
  8. 2024DELTA: Decomposed Efficient Long-Term Robot Task Planning using Large Language Modelscited by 33
  9. 2023Interactive Planning Using Large Language Models for Partially Observable Robotics Taskscited by 27
  10. 2024A Survey of Robotic Language Grounding: Tradeoffs between Symbols and Embeddingscited by 24
  11. 2023Conformal Temporal Logic Planning using Large Language Modelscited by 21
  12. 2024SELP: Generating Safe and Efficient Task Plans for Robot Agents with Large Language Modelscited by 21
  13. 2024A Survey of Optimization-based Task and Motion Planning: From Classical To Learning Approachescited by 20
  14. 2026Show-Harness: Just a VLM Agent Can Play Robotscited by 20
  15. 2024AutoGPT+P: Affordance-based Task Planning with Large Language Modelscited by 19
  16. 2024Trust the PRoC3S: Solving Long-Horizon Robotics Problems with LLMs and Constraint Satisfactioncited by 19
  17. 2024SPINE: Online Semantic Planning for Missions with Incomplete Natural Language Specifications in Unstructured Environmentscited by 18
  18. 2025A Survey on Large Language Models for Automated Planningcited by 17
  19. 2025Constrained Discrete Diffusioncited by 17
  20. 2024Generating consistent PDDL domains with Large Language Modelscited by 15
  21. 2025Learning Compositional Behaviors from Demonstration and Languagecited by 15
  22. 2024DKPROMPT: Domain Knowledge Prompting Vision-Language Models for Open-World Planningcited by 14
  23. 2025Generative Multi-Agent Collaboration in Embodied AI: A Systematic Reviewcited by 13
  24. 2025Exploring Embodied Multimodal Large Models: Development, Datasets, and Future Directionscited by 13
  25. 2025Enhancing Multi-Agent Systems via Reinforcement Learning with LLM-based Planner and Graph-based Policycited by 12
  26. 2025CodeSteer: Symbolic-Augmented Language Models via Code/Text Guidancecited by 11
  27. 2025ConformalNL2LTL: Translating Natural Language Instructions into Temporal Logic Formulas with Conformal Correctness Guaranteescited by 11
  28. 2025Air-Ground Collaboration for Language-Specified Missions in Unknown Environmentscited by 11
  29. 2024FLAIR: Feeding via Long-horizon AcquIsition of Realistic dishescited by 10
  30. 2025Code-as-Symbolic-Planner: Foundation Model-Based Robot Planning via Symbolic Code Generationcited by 10
  31. 2025Distilling On-device Language Models for Robot Planning with Minimal Human Interventioncited by 10
  32. 2024Open-World Task and Motion Planning via Vision-Language Model Generated Constraintscited by 9
  33. 2025Multi-Agent Systems for Robotic Autonomy with LLMscited by 9
  34. 2026Why Do LLM-based Web Agents Fail? A Hierarchical Planning Perspectivecited by 9
  35. 2024Retrieval-Augmented Hierarchical in-Context Reinforcement Learning and Hindsight Modular Reflections for Task Planning with LLMscited by 8
  36. 2025ASHiTA: Automatic Scene-grounded HIerarchical Task Analysiscited by 8
  37. 2024Look Further Ahead: Testing the Limits of GPT-4 in Path Planningcited by 7
  38. 2025LLM+MAP: Bimanual Robot Task Planning using Large Language Models and Planning Domain Definition Languagecited by 7

50 most cited of 113 All 113 (JSON)