# Closed-Loop Open-Vocabulary Mobile Manipulation with GPT-4V

arXiv: https://arxiv.org/abs/2404.10220 · HTML: https://mlsanity.com/paper/2404.10220/

## Key facts

- Cited by 40 papers in the map (data through 14 Oct 2026).
- Builds on 38 of its 65 references that are in the map.
- First posted on arXiv 16 Apr 2024.

## Builds on (most cited first, 30 of 38)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021)
- [RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control](https://mlsanity.com/paper/2307.15818/) (2023)
- [Voyager: An Open-Ended Embodied Agent with Large Language Models](https://mlsanity.com/paper/2305.16291/) (2023)
- [RT-1: Robotics Transformer for Real-World Control at Scale](https://mlsanity.com/paper/2212.06817/) (2022)
- [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)
- [Grounded SAM: Assembling Open-World Models for Diverse Visual Tasks](https://mlsanity.com/paper/2401.14159/) (2024)
- [ProgPrompt: Generating Situated Robot Task Plans using Large Language Models](https://mlsanity.com/paper/2209.11302/) (2022)
- [Target-driven Visual Navigation in Indoor Scenes using Deep Reinforcement Learning](https://mlsanity.com/paper/1609.05143/) (2016)
- [ConceptGraphs: Open-Vocabulary 3D Scene Graphs for Perception and Planning](https://mlsanity.com/paper/2309.16650/) (2023)
- [SayPlan: Grounding Large Language Models using 3D Scene Graphs for Scalable Robot Task Planning](https://mlsanity.com/paper/2307.06135/) (2023)
- [Contact-GraspNet: Efficient 6-DoF Grasp Generation in Cluttered Scenes](https://mlsanity.com/paper/2103.14127/) (2021)
- [Foundation Models in Robotics: Applications, Challenges, and the Future](https://mlsanity.com/paper/2312.07843/) (2023)
- [A Survey of Vision-Language Pre-Trained Models](https://mlsanity.com/paper/2202.10936/) (2022)
- [REFLECT: Summarizing Robot Experiences for Failure Explanation and Correction](https://mlsanity.com/paper/2306.15724/) (2023)
- [Look Before You Leap: Unveiling the Power of GPT-4V in Robotic Vision-Language Planning](https://mlsanity.com/paper/2311.17842/) (2023)
- [3D Dynamic Scene Graphs: Actionable Spatial Perception with Places, Objects, and Humans](https://mlsanity.com/paper/2002.06289/) (2020)
- [Large Language Models for Robotics: A Survey](https://mlsanity.com/paper/2311.07226/) (2023)
- [Navigating to Objects in the Real World](https://mlsanity.com/paper/2212.00922/) (2022)
- [HomeRobot: Open-Vocabulary Mobile Manipulation](https://mlsanity.com/paper/2306.11565/) (2023)
- [Reducing the Barrier to Entry of Complex Robotic Software: a MoveIt! Case Study](https://mlsanity.com/paper/1404.3785/) (2014)
- [GOAT: GO to Any Thing](https://mlsanity.com/paper/2311.06430/) (2023)
- [GPT-4V(ision) for Robotics: Multimodal Task Planning from Human Demonstration](https://mlsanity.com/paper/2311.12015/) (2023)
- [RTAB-Map as an Open-Source Lidar and Visual SLAM Library for Large-Scale and Long-Term Online Operation](https://mlsanity.com/paper/2403.06341/) (2024)
- [Online Replanning in Belief Space for Partially Observable Task and Motion Problems](https://mlsanity.com/paper/1911.04577/) (2019)
- [DoReMi: Grounding Language Model by Detecting and Recovering from Plan-Execution Misalignment](https://mlsanity.com/paper/2307.00329/) (2023)

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

- [π0.5: a Vision-Language-Action Model with Open-World Generalization](https://mlsanity.com/paper/2504.16054/) (2025)
- [A Survey on Vision-Language-Action Models for Embodied AI](https://mlsanity.com/paper/2405.14093/) (2024)
- [Hi Robot: Open-Ended Instruction Following with Hierarchical Vision-Language-Action Models](https://mlsanity.com/paper/2502.19417/) (2025)
- [OneTwoVLA: A Unified Vision-Language-Action Model with Adaptive Reasoning](https://mlsanity.com/paper/2505.11917/) (2025)
- [Pure Vision Language Action (VLA) Models: A Comprehensive Survey](https://mlsanity.com/paper/2509.19012/) (2025)
- [BUMBLE: Unifying Reasoning and Acting with Vision-Language Models for Building-wide Mobile Manipulation](https://mlsanity.com/paper/2410.06237/) (2024)
- [3D CAVLA: Leveraging Depth and 3D Context to Generalize Vision Language Action Models for Unseen Tasks](https://mlsanity.com/paper/2505.05800/) (2025)
- [Robix: A Unified Model for Robot Interaction, Reasoning and Planning](https://mlsanity.com/paper/2509.01106/) (2025)
- [DKPROMPT: Domain Knowledge Prompting Vision-Language Models for Open-World Planning](https://mlsanity.com/paper/2406.17659/) (2024)
- [MoMa-Kitchen: A 100K+ Benchmark for Affordance-Grounded Last-Mile Navigation in Mobile Manipulation](https://mlsanity.com/paper/2503.11081/) (2025)
- [EmbodiedVSR: Dynamic Scene Graph-Guided Chain-of-Thought Reasoning for Visual Spatial Tasks](https://mlsanity.com/paper/2503.11089/) (2025)
- [Masked Point-Entity Contrast for Open-Vocabulary 3D Scene Understanding](https://mlsanity.com/paper/2504.19500/) (2025)
- [MORE: Mobile Manipulation Rearrangement Through Grounded Language Reasoning](https://mlsanity.com/paper/2505.03035/) (2025)
- [ODYSSEY: Open-World Quadrupeds Exploration and Manipulation for Long-Horizon Tasks](https://mlsanity.com/paper/2508.08240/) (2025)
- [Maestro: Orchestrating Robotics Modules with Vision-Language Models for Zero-Shot Generalist Robots](https://mlsanity.com/paper/2511.00917/) (2025)
- [DISCO: Language-Guided Manipulation with Diffusion Policies and Constrained Inpainting](https://mlsanity.com/paper/2406.09767/) (2024)
- [ConceptAgent: LLM-Driven Precondition Grounding and Tree Search for Robust Task Planning and Execution](https://mlsanity.com/paper/2410.06108/) (2024)
- [MANSION: Multi-floor lANguage-to-3D Scene generatIOn for loNg-horizon tasks](https://mlsanity.com/paper/2603.11554/) (2026)
- [HiFi-CS: Towards Open Vocabulary Visual Grounding For Robotic Grasping Using Vision-Language Models](https://mlsanity.com/paper/2409.10419/) (2024)
- [AlignBot: Aligning VLM-powered Customized Task Planning with User Reminders Through Fine-Tuning for Household Robots](https://mlsanity.com/paper/2409.11905/) (2024)
- [Growing with Your Embodied Agent: A Human-in-the-Loop Lifelong Code Generation Framework for Long-Horizon Manipulation Skills](https://mlsanity.com/paper/2509.18597/) (2025)
- [Learning Human-Humanoid Coordination for Collaborative Object Carrying](https://mlsanity.com/paper/2510.14293/) (2025)
- [See, Plan, Rewind: Progress-Aware Vision-Language-Action Models for Robust Robotic Manipulation](https://mlsanity.com/paper/2603.09292/) (2026)
- [M3Bench: Benchmarking Whole-body Motion Generation for Mobile Manipulation in 3D Scenes](https://mlsanity.com/paper/2410.06678/) (2024)
- [WildLMa: Long Horizon Loco-Manipulation in the Wild](https://mlsanity.com/paper/2411.15131/) (2024)
- [DaDu-E: Rethinking the Role of Large Language Model in Robotic Computing Pipeline](https://mlsanity.com/paper/2412.01663/) (2024)
- [OWMM-Agent: Open World Mobile Manipulation With Multi-modal Agentic Data Synthesis](https://mlsanity.com/paper/2506.04217/) (2025)
- [ESPIRE: A Diagnostic Benchmark for Embodied Spatial Reasoning of Vision-Language Models](https://mlsanity.com/paper/2603.13033/) (2026)
- [Long-horizon Embodied Planning with Implicit Logical Inference and Hallucination Mitigation](https://mlsanity.com/paper/2409.15658/) (2024)
- [LightPlanner: Unleashing the Reasoning Capabilities of Lightweight Large Language Models in Task Planning](https://mlsanity.com/paper/2503.08508/) (2025)

## Abstract (arXiv)

Autonomous robot navigation and manipulation in open environments require reasoning and replanning with closed-loop feedback. In this work, we present COME-robot, the first closed-loop robotic system utilizing the GPT-4V vision-language foundation model for open-ended reasoning and adaptive planning in real-world scenarios.COME-robot incorporates two key innovative modules: (i) a multi-level open-vocabulary perception and situated reasoning module that enables effective exploration of the 3D environment and target object identification using commonsense knowledge and situated information, and (ii) an iterative closed-loop feedback and restoration mechanism that verifies task feasibility, monitors execution success, and traces failure causes across different modules for robust failure recovery. Through comprehensive experiments involving 8 challenging real-world mobile and tabletop manipulation tasks, COME-robot demonstrates a significant improvement in task success rate (~35%) compared to state-of-the-art methods. We further conduct comprehensive analyses to elucidate how COME-robot's design facilitates failure recovery, free-form instruction following, and long-horizon task planning.