GraspCorrect：通过视觉-语言模型引导的反馈实现机器人抓取校正
GraspCorrect: Robotic Grasp Correction via Vision-Language Model-Guided Feedback
AI总结：
针对机器人抓取不稳定导致任务失败的问题，提出即插即用模块GraspCorrect，通过视觉-语言模型引导的迭代反馈框架，结合抓取引导提示与物体感知采样，提升现有策略模型的抓取稳定性与任务成功率。
摘要：
尽管机器人操作领域已取得显著进展，实现一致且稳定的抓取仍是一项核心挑战，这往往会限制复杂任务的成功执行。我们的分析显示，即便是最先进的策略模型也经常表现出不稳定的抓取行为，由此产生的失败案例成为现实世界机器人应用的瓶颈。为应对这些挑战，我们提出了GraspCorrect，这是一个即插即用模块，旨在通过视觉-语言模型引导的反馈提升抓取性能。GraspCorrect采用迭代式视觉问答框架，包含两个关键组件：融入任务特定约束的抓取引导提示，以及确保选出物理可行候选抓取方案的物体感知采样。通过迭代生成中间视觉目标并将其转换为关节级动作，GraspCorrect在RLBench和CALVIN数据集上显著提升了抓取稳定性，且持续提高了现有策略模型的任务成功率。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
