mlsanityData to 14 Oct 2026

Errors are Useful Prompts: Instruction Guided Task Programming with Verifier-Assisted Iterative Prompting

arXiv 2303.14100 ↗First posted 24 Mar 2023cs.ROcited by 46

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Abstract (arXiv)

Generating low-level robot task plans from high-level natural language instructions remains a challenging problem. Although large language models have shown promising results in generating plans, the accuracy of the output remains unverified. Furthermore, the lack of domain-specific language data poses a limitation on the applicability of these models. In this paper, we propose CLAIRIFY, a novel approach that combines automatic iterative prompting with program verification to ensure programs written in data-scarce domain-specific language are syntactically valid and incorporate environment constraints. Our approach provides effective guidance to the language model on generating structured-like task plans by incorporating any errors as feedback, while the verifier ensures the syntactic accuracy of the generated plans. We demonstrate the effectiveness of CLAIRIFY in planning chemistry experiments by achieving state-of-the-art results. We also show that the generated plans can be executed on a real robot by integrating them with a task and motion planner.

Key facts. Cited by 46 papers in the map (data through 14 Oct 2026). First posted on arXiv 24 Mar 2023.

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Citing papers per year: 2023: 15; 2024: 14; 2025: 11; 2026: 6.

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  1. 2023Voyager: An Open-Ended Embodied Agent with Large Language Modelscited by 2,191
  2. 2023MetaGPT: Meta Programming for A Multi-Agent Collaborative Frameworkcited by 1,808
  3. 2023SayPlan: Grounding Large Language Models using 3D Scene Graphs for Scalable Robot Task Planningcited by 294
  4. 2023OctoPack: Instruction Tuning Code Large Language Modelscited by 189
  5. 2023REFLECT: Summarizing Robot Experiences for Failure Explanation and Correctioncited by 175
  6. 2023GenSim: Generating Robotic Simulation Tasks via Large Language Modelscited by 125
  7. 2023AutoTAMP: Autoregressive Task and Motion Planning with LLMs as Translators and Checkerscited by 113
  8. 2023Foundational Models Defining a New Era in Vision: A Survey and Outlookcited by 110
  9. 2024Code-as-Monitor: Constraint-aware Visual Programming for Reactive and Proactive Robotic Failure Detectioncited by 63
  10. 2024ORGANA: A Robotic Assistant for Automated Chemistry Experimentation and Characterizationcited by 61
  11. 2024Physics of Language Models: Part 2.2, How to Learn From Mistakes on Grade-School Math Problemscited by 50
  12. 2025LLM Collaboration With Multi-Agent Reinforcement Learningcited by 49
34 more
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  2. 2024Steering Large Language Models between Code Execution and Textual Reasoningcited by 24
  3. 2024A Self-Correcting Vision-Language-Action Model for Fast and Slow System Manipulationcited by 21
  4. 2024AIC MLLM: Autonomous Interactive Correction MLLM for Robust Robotic Manipulationcited by 20
  5. 2023Incremental Learning of Humanoid Robot Behavior from Natural Interaction and Large Language Modelscited by 19
  6. 2024LangProp: A code optimization framework using Large Language Models applied to drivingcited by 18
  7. 2023LLM-State: Open World State Representation for Long-horizon Task Planning with Large Language Modelcited by 15
  8. 2024Evaluating Uncertainty-based Failure Detection for Closed-Loop LLM Plannerscited by 15
  9. 2023Sasha: Creative Goal-Oriented Reasoning in Smart Homes with Large Language Modelscited by 11
  10. 2024Cohesive Conversations: Enhancing Authenticity in Multi-Agent Simulated Dialoguescited by 11
  11. 2026QCalEval: Benchmarking Vision-Language Models for Quantum Calibration Plot Understandingcited by 11
  12. 2025Code-as-Symbolic-Planner: Foundation Model-Based Robot Planning via Symbolic Code Generationcited by 10
  13. 2023Selective Perception: Optimizing State Descriptions with Reinforcement Learning for Language Model Actorscited by 9
  14. 2024ReFF: Reinforcing Format Faithfulness in Language Models across Varied Taskscited by 9
  15. 2025Hierarchical Planning for Complex Tasks with Knowledge Graph-RAG and Symbolic Verificationcited by 9
  16. 2024ConceptAgent: LLM-Driven Precondition Grounding and Tree Search for Robust Task Planning and Executioncited by 8
  17. 2025Learning Game-Playing Agents with Generative Code Optimizationcited by 8
  18. 2026Learning Decentralized LLM Collaboration with Multi-Agent Actor Criticcited by 8
  19. 2025Structured Preference Optimization for Vision-Language Long-Horizon Task Planningcited by 7
  20. 2024RAGent: Retrieval-based Access Control Policy Generationcited by 5
  21. 2025Schema-Guided Scene-Graph Reasoning based on Multi-Agent Large Language Model Systemcited by 5
  22. 2025Collision- and Reachability-Aware Multi-Robot Control with Grounded LLM Plannerscited by 4
  23. 2025Prime the search: Using large language models for guiding geometric task and motion planning by warm-starting tree searchcited by 4
  24. 2023Is Feedback All You Need? Leveraging Natural Language Feedback in Goal-Conditioned Reinforcement Learningcited by 2
  25. 2024Real-World Cooking Robot System from Recipes Based on Food State Recognition Using Foundation Models and PDDLcited by 2
  26. 2025Self-Corrective Task Planning by Inverse Prompting with Large Language Modelscited by 2
  27. 2024TimelyLLM: Segmented LLM Serving System for Time-sensitive Robotic Applicationscited by 1
  28. 2025Constrained Natural Language Action Planning for Resilient Embodied Systemscited by 1
  29. 2026Safe Embodied AI for Long-horizon Tasks: A Cross-layer Analysis of Robotic Manipulationcited by 1
  30. 2023Lessons in Reproducibility: Insights from NLP Studies in Materials Sciencecited by 0
  31. 2025AI-Assisted Authoring for Transparent, Data-Driven Documentscited by 0
  32. 2026Navigating the Clutter: Waypoint-Based Bi-Level Planning for Multi-Robot Systemscited by 0
  33. 2026What Language Does and What the Evidence Supports: A Functional Role Taxonomy and Evidence Audit of Language Grounding in Embodied Agentscited by 0
  34. 2026CAPEX: Efficiently Distilling Foundation Model Behavior into Deployable Robot Policies through Experience-Adaptive Reasoningcited by 0