mlsanityData to 7 Oct 2026

GraspCorrect: Robotic Grasp Correction via Vision-Language Model-Guided Feedback

arXiv 2503.15035 ↗First posted 19 Mar 2025cs.AI cs.ROcited by 2

On the map: Multimodal LLMs / vision-language models → Embodied and 3D vision-language models

Abstract (arXiv)

Despite significant advancements in robotic manipulation, achieving consistent and stable grasping remains a fundamental challenge, often limiting the successful execution of complex tasks. Our analysis reveals that even state-of-the-art policy models frequently exhibit unstable grasping behaviors, leading to failure cases that create bottlenecks in real-world robotic applications. To address these challenges, we introduce GraspCorrect, a plug-and-play module designed to enhance grasp performance through vision-language model-guided feedback. GraspCorrect employs an iterative visual question-answering framework with two key components: grasp-guided prompting, which incorporates task-specific constraints, and object-aware sampling, which ensures the selection of physically feasible grasp candidates. By iteratively generating intermediate visual goals and translating them into joint-level actions, GraspCorrect significantly improves grasp stability and consistently enhances task success rates across existing policy models in the RLBench and CALVIN datasets.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Placed in Embodied and 3D vision-language models, one of 16 directions in Multimodal LLMs / vision-language models. Builds on 35 of its 41 references that are in the map. First posted on arXiv 19 Mar 2025.

Builds on

35 of 41 references.

Closest ancestors · 5

Same direction, newest first

  1. 2024Sparkle: Mastering Basic Spatial Capabilities in Vision Language Models Elicits Generalization to Spatial Reasoning21 Oct
  2. 2024PIVOT: Iterative Visual Prompting Elicits Actionable Knowledge for VLMs12 Feb
  3. 2024SpatialVLM: Endowing Vision-Language Models with Spatial Reasoning Capabilities22 Jan
  4. 2023EmbodiedGPT: Vision-Language Pre-Training via Embodied Chain of Thought24 May
  5. 2023PaLM-E: An Embodied Multimodal Language Model6 Mar

Other references · 30

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  3. 2023Segment Anythingcited by 8,158
  4. 2021Zero-Shot Text-to-Image Generationcited by 4,276
  5. 2016Gaussian Error Linear Units (GELUs)cited by 3,431
  6. 2023Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detectioncited by 3,274
  7. 2022InstructPix2Pix: Learning to Follow Image Editing Instructionscited by 2,591
  8. 2022RT-1: Robotics Transformer for Real-World Control at Scalecited by 2,007
  9. 2018Group Normalizationcited by 1,514
  10. 2022Imagic: Text-Based Real Image Editing with Diffusion Modelscited by 1,172
  11. 2021Resolution-robust Large Mask Inpainting with Fourier Convolutionscited by 730
  12. 2022R3M: A Universal Visual Representation for Robot Manipulationcited by 678
18 more
  1. 2019RLBench: The Robot Learning Benchmark & Learning Environmentcited by 669
  2. 2022DiffEdit: Diffusion-based semantic image editing with mask guidancecited by 643
  3. 2023BridgeData V2: A Dataset for Robot Learning at Scalecited by 634
  4. 2021CALVIN: A Benchmark for Language-Conditioned Policy Learning for Long-Horizon Robot Manipulation Taskscited by 564
  5. 2022Perceiver-Actor: A Multi-Task Transformer for Robotic Manipulationcited by 545
  6. 20243D Diffuser Actor: Policy Diffusion with 3D Scene Representationscited by 278
  7. 2023Zero-Shot Robotic Manipulation with Pretrained Image-Editing Diffusion Modelscited by 264
  8. 2023RVT: Robotic View Transformer for 3D Object Manipulationcited by 224
  9. 2021Contact-GraspNet: Efficient 6-DoF Grasp Generation in Cluttered Scenescited by 213
  10. 2023Osprey: Pixel Understanding with Visual Instruction Tuningcited by 158
  11. 2024Deep Reinforcement Learning for Robotics: A Survey of Real-World Successescited by 153
  12. 2024RVT-2: Learning Precise Manipulation from Few Demonstrationscited by 135
  13. 2024THE COLOSSEUM: A Benchmark for Evaluating Generalization for Robotic Manipulationcited by 128
  14. 2020Learning to Generalize Across Long-Horizon Tasks from Human Demonstrationscited by 114
  15. 2024CoPa: General Robotic Manipulation through Spatial Constraints of Parts with Foundation Modelscited by 97
  16. 2023Language Models as Zero-Shot Trajectory Generatorscited by 59
  17. 2024Efficient Diffusion Transformer Policies with Mixture of Expert Denoisers for Multitask Learningcited by 46
  18. 2024GR-MG: Leveraging Partially Annotated Data via Multi-Modal Goal-Conditioned Policycited by 39

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2025Spatial Policy: Guiding Visuomotor Robotic Manipulation with Spatial-Aware Modeling and Reasoningcited by 9
  2. 2026Grasp-Then-Plan with Failure Attribution: A Closed Two-Stage Framework for Precise and Generalizable Robotic Manipulationcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Training-free Task-oriented Grasp Generation
  2. 2025DexGraspVLA: A Vision-Language-Action Framework Towards General Dexterous Grasping
  3. 2025MapleGrasp: Mask-guided Feature Pooling for Language-driven Efficient Robotic Grasping
  4. 2026AdaClearGrasp: Learning Adaptive Clearing for Zero-Shot Robust Dexterous Grasping in Densely Cluttered Environments ↗
  5. 2026ProbeAct: Probe-Guided Training-Free Failure Recovery in Vision-Language-Action Models
  6. 2024A Parameter-Efficient Tuning Framework for Language-guided Object Grounding and Robot Grasping
  7. 2024Reasoning Grasping via Multimodal Large Language Model
  8. 2025VCoT-Grasp: Grasp Foundation Models with Visual Chain-of-Thought Reasoning for Language-driven Grasp Generation