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

• URL: http://arxiv.org/abs/2503.15035v1
• Date: Wed, 19 Mar 2025 09:25:32 GMT
• Title: GraspCorrect: Robotic Grasp Correction via Vision-Language Model-Guided Feedback
• Authors: Sungjae Lee, Yeonjoo Hong, Kwang In Kim,
• Abstract summary: Even state-of-the-art policy models frequently exhibit unstable grasping behaviors.<n>We introduce GraspCorrect, a plug-and-play module designed to enhance grasp performance through vision-language model-guided feedback.
• Score: 23.48582504679409
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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