# CoPAL: Corrective Planning of Robot Actions with Large Language Models

arXiv: https://arxiv.org/abs/2310.07263 · HTML: https://mlsanity.com/paper/2310.07263/

## Key facts

- Cited by 40 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 42 references that are in the map.
- First posted on arXiv 11 Oct 2023.

## Builds on (most cited first, 29 of 29)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Do As I Can, Not As I Say: Grounding Language in Robotic Affordances](https://mlsanity.com/paper/2204.01691/) (2022)
- [PaLM-E: An Embodied Multimodal Language Model](https://mlsanity.com/paper/2303.03378/) (2023)
- [Code as Policies: Language Model Programs for Embodied Control](https://mlsanity.com/paper/2209.07753/) (2022)
- [Inner Monologue: Embodied Reasoning through Planning with Language Models](https://mlsanity.com/paper/2207.05608/) (2022)
- [Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents](https://mlsanity.com/paper/2201.07207/) (2022)
- [ProgPrompt: Generating Situated Robot Task Plans using Large Language Models](https://mlsanity.com/paper/2209.11302/) (2022)
- [LLM+P: Empowering Large Language Models with Optimal Planning Proficiency](https://mlsanity.com/paper/2304.11477/) (2023)
- [Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond](https://mlsanity.com/paper/2304.13712/) (2023)
- [Describe, Explain, Plan and Select: Interactive Planning with Large Language Models Enables Open-World Multi-Task Agents](https://mlsanity.com/paper/2302.01560/) (2023)
- [On the Planning Abilities of Large Language Models : A Critical Investigation](https://mlsanity.com/paper/2305.15771/) (2023)
- [ChatGPT for Robotics: Design Principles and Model Abilities](https://mlsanity.com/paper/2306.17582/) (2023)
- [SayPlan: Grounding Large Language Models using 3D Scene Graphs for Scalable Robot Task Planning](https://mlsanity.com/paper/2307.06135/) (2023)
- [Integrated Task and Motion Planning](https://mlsanity.com/paper/2010.01083/) (2020)
- [Large Language Models as Commonsense Knowledge for Large-Scale Task Planning](https://mlsanity.com/paper/2305.14078/) (2023)
- [Text2Motion: From Natural Language Instructions to Feasible Plans](https://mlsanity.com/paper/2303.12153/) (2023)
- [Pre-Trained Language Models for Interactive Decision-Making](https://mlsanity.com/paper/2202.01771/) (2022)
- [TidyBot: Personalized Robot Assistance with Large Language Models](https://mlsanity.com/paper/2305.05658/) (2023)
- [Leveraging Pre-trained Large Language Models to Construct and Utilize World Models for Model-based Task Planning](https://mlsanity.com/paper/2305.14909/) (2023)
- [RoboAgent: Generalization and Efficiency in Robot Manipulation via Semantic Augmentations and Action Chunking](https://mlsanity.com/paper/2309.01918/) (2023)
- [Foundation Models for Decision Making: Problems, Methods, and Opportunities](https://mlsanity.com/paper/2303.04129/) (2023)
- [Task and Motion Planning with Large Language Models for Object Rearrangement](https://mlsanity.com/paper/2303.06247/) (2023)
- [Translating Natural Language to Planning Goals with Large-Language Models](https://mlsanity.com/paper/2302.05128/) (2023)
- [ChatGPT Empowered Long-Step Robot Control in Various Environments: A Case Application](https://mlsanity.com/paper/2304.03893/) (2023)
- [Statler: State-Maintaining Language Models for Embodied Reasoning](https://mlsanity.com/paper/2306.17840/) (2023)
- [Integrating Action Knowledge and LLMs for Task Planning and Situation Handling in Open Worlds](https://mlsanity.com/paper/2305.17590/) (2023)
- [Generating Executable Action Plans with Environmentally-Aware Language Models](https://mlsanity.com/paper/2210.04964/) (2022)
- [A Glimpse in ChatGPT Capabilities and its impact for AI research](https://mlsanity.com/paper/2305.06087/) (2023)
- [Learning Type-Generalized Actions for Symbolic Planning](https://mlsanity.com/paper/2308.04867/) (2023)

## Built on by (most cited first, 30 of 40)

- [VSP: Assessing the dual challenges of perception and reasoning in spatial planning tasks for VLMs](https://mlsanity.com/paper/2407.01863/) (2024)
- [Guiding Long-Horizon Task and Motion Planning with Vision Language Models](https://mlsanity.com/paper/2410.02193/) (2024)
- [Can We Trust Embodied Agents? Exploring Backdoor Attacks against Embodied LLM-based Decision-Making Systems](https://mlsanity.com/paper/2405.20774/) (2024)
- [Probabilistically Correct Language-based Multi-Robot Planning using Conformal Prediction](https://mlsanity.com/paper/2402.15368/) (2024)
- [AnyBimanual: Transferring Unimanual Policy for General Bimanual Manipulation](https://mlsanity.com/paper/2412.06779/) (2024)
- [Generating consistent PDDL domains with Large Language Models](https://mlsanity.com/paper/2404.07751/) (2024)
- [Large Language Model-Enabled Multi-Agent Manufacturing Systems](https://mlsanity.com/paper/2406.01893/) (2024)
- [DAG-Plan: Generating Directed Acyclic Dependency Graphs for Dual-Arm Cooperative Planning](https://mlsanity.com/paper/2406.09953/) (2024)
- [Traffic Scene Generation from Natural Language Description for Autonomous Vehicles with Large Language Model](https://mlsanity.com/paper/2409.09575/) (2024)
- [From Idea to CAD: A Language Model-Driven Multi-Agent System for Collaborative Design](https://mlsanity.com/paper/2503.04417/) (2025)
- [To Help or Not to Help: LLM-based Attentive Support for Human-Robot Group Interactions](https://mlsanity.com/paper/2403.12533/) (2024)
- [Tulip Agent -- Enabling LLM-Based Agents to Solve Tasks Using Large Tool Libraries](https://mlsanity.com/paper/2407.21778/) (2024)
- [AlignBot: Aligning VLM-powered Customized Task Planning with User Reminders Through Fine-Tuning for Household Robots](https://mlsanity.com/paper/2409.11905/) (2024)
- [VeriGraph: Scene Graphs for Execution Verifiable Robot Planning](https://mlsanity.com/paper/2411.10446/) (2024)
- [What Breaks Embodied AI Security:LLM Vulnerabilities, CPS Flaws,or Something Else?](https://mlsanity.com/paper/2602.17345/) (2026)
- [DaDu-E: Rethinking the Role of Large Language Model in Robotic Computing Pipeline](https://mlsanity.com/paper/2412.01663/) (2024)
- [TAPE: Tool-Guided Adaptive Planning and Constrained Execution in Language Model Agents](https://mlsanity.com/paper/2602.19633/) (2026)
- [Context Matters! Relaxing Goals with LLMs for Feasible 3D Scene Planning](https://mlsanity.com/paper/2506.15828/) (2025)
- [Meta-Control: Automatic Model-based Control Synthesis for Heterogeneous Robot Skills](https://mlsanity.com/paper/2405.11380/) (2024)
- [Self-Corrective Task Planning by Inverse Prompting with Large Language Models](https://mlsanity.com/paper/2503.07317/) (2025)
- [RAIDER: Tool-Equipped Large Language Model Agent for Robotic Action Issue Detection, Explanation and Recovery](https://mlsanity.com/paper/2503.17703/) (2025)
- [Achieving Scalable Robot Autonomy via neurosymbolic planning using lightweight local LLM](https://mlsanity.com/paper/2505.08492/) (2025)
- [RoboPARA: Dual-Arm Robot Planning with Parallel Allocation and Recomposition Across Tasks](https://mlsanity.com/paper/2506.06683/) (2025)
- [An LLM-powered Natural-to-Robotic Language Translation Framework with Correctness Guarantees](https://mlsanity.com/paper/2508.19074/) (2025)
- [Information Seeking for Robust Decision Making under Partial Observability](https://mlsanity.com/paper/2510.01531/) (2025)
- [MALLVI: A Multi-Agent Framework for Integrated Generalized Robotics Manipulation](https://mlsanity.com/paper/2602.16898/) (2026)
- [HyperGraphOS: A Meta Operating System for Science and Engineering](https://mlsanity.com/paper/2412.04923/) (2024)
- [Perceiving, Reasoning, Adapting: A Dual-Layer Framework for VLM-Guided Precision Robotic Manipulation](https://mlsanity.com/paper/2503.05064/) (2025)
- [Leveraging Pre-trained Large Language Models with Refined Prompting for Online Task and Motion Planning](https://mlsanity.com/paper/2504.21596/) (2025)
- [CARMA: Context-Aware Situational Grounding of Human-Robot Group Interactions by Combining Vision-Language Models with Object and Action Recognition](https://mlsanity.com/paper/2506.20373/) (2025)

## Abstract (arXiv)

In the pursuit of fully autonomous robotic systems capable of taking over tasks traditionally performed by humans, the complexity of open-world environments poses a considerable challenge. Addressing this imperative, this study contributes to the field of Large Language Models (LLMs) applied to task and motion planning for robots. We propose a system architecture that orchestrates a seamless interplay between multiple cognitive levels, encompassing reasoning, planning, and motion generation. At its core lies a novel replanning strategy that handles physically grounded, logical, and semantic errors in the generated plans. We demonstrate the efficacy of the proposed feedback architecture, particularly its impact on executability, correctness, and time complexity via empirical evaluation in the context of a simulation and two intricate real-world scenarios: blocks world, barman and pizza preparation.