Closed-Loop Open-Vocabulary Mobile Manipulation with GPT-4V
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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.
Key facts. Cited by 40 papers in the map (data through 7 Oct 2026). Builds on 38 of its 65 references that are in the map. First posted on arXiv 16 Apr 2024.
Builds on
38 of 65 references.
References · 38
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- 2020Language Models are Few-Shot Learnerscited by 32,199
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- 2023ConceptGraphs: Open-Vocabulary 3D Scene Graphs for Perception and Planningcited by 357
- 2016Target-driven Visual Navigation in Indoor Scenes using Deep Reinforcement Learningcited by 351
- 2023SayPlan: Grounding Large Language Models using 3D Scene Graphs for Scalable Robot Task Planningcited by 289
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- 2022A Survey of Vision-Language Pre-Trained Modelscited by 177
- 2023REFLECT: Summarizing Robot Experiences for Failure Explanation and Correctioncited by 171
- 2023Look Before You Leap: Unveiling the Power of GPT-4V in Robotic Vision-Language Planningcited by 161
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- 2023Large Language Models for Robotics: A Surveycited by 132
- 2022Navigating to Objects in the Real Worldcited by 104
- 2023HomeRobot: Open-Vocabulary Mobile Manipulationcited by 104
- 2023GOAT: GO to Any Thingcited by 89
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- 2023DoReMi: Grounding Language Model by Detecting and Recovering from Plan-Execution Misalignmentcited by 58
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- 2021Long-Horizon Manipulation of Unknown Objects via Task and Motion Planning with Estimated Affordancescited by 38
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Citing papers per year: 2024: 12; 2025: 22; 2026: 6.
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