CLASP: 闭环异步空间感知用于开放词汇桌面物体抓取
CLASP: Closed-loop Asynchronous Spatial Perception for Open-vocabulary Desktop Object Grasping
AI总结：
本文提出CLASP框架，通过异步闭环机制整合多模态感知、逻辑推理与状态反馈，解决低级抓取中多模态演示不足、空间幻觉和开放环执行脆弱性问题，实现87%的成功率和强泛化能力。
摘要：
桌面物体抓取在智能制造、物流和农业中广泛应用。尽管视觉-语言模型（VLMs）在机器人操作中显示出潜力，但其在低级抓取中的部署面临关键挑战：多模态高质量演示稀缺、弱几何接地导致的空间幻觉，以及动态环境中开放环执行的脆弱性。为解决这些挑战，我们提出CLASP，一种新颖的异步闭环框架，整合多模态感知、逻辑推理和状态反馈。首先，设计双路径分层感知模块，将高层语义意图与几何接地解耦，指导推理模型输出和确定性动作元组，减少空间幻觉。其次，实现异步闭环评估器，比较执行前后状态，提供文本反馈以建立稳健的错误纠正循环，提高传统开放环执行在动态环境中的鲁棒性。最后，设计可扩展的多模态数据引擎，自动从真实和合成场景生成高质量空间注释和推理模板，无需人工远程操作。大量实验表明，我们的方法显著优于现有基线，达到87.0%的整体成功率。值得注意的是，所提框架在多样化物体上表现出卓越的泛化能力，弥合了仿真到现实的差距，并在几何挑战类别和杂乱场景中提供出色的鲁棒性。
英文摘要：
Robot grasping of desktop object is widely used in intelligent manufacturing, logistics, and agriculture.Although vision-language models (VLMs) show strong potential for robotic manipulation, their deployment in low-level grasping faces key challenges: scarce high-quality multimodal demonstrations, spatial hallucination caused by weak geometric grounding, and the fragility of open-loop execution in dynamic environments. To address these challenges, we propose Closed-Loop Asynchronous Spatial Perception(CLASP), a novel asynchronous closed-loop framework that integrates multimodal perception, logical reasoning, and state-reflective feedback. First, we design a Dual-Pathway Hierarchical Perception module that decouples high-level semantic intent from geometric grounding. The design guides the output of the inference model and the definite action tuples, reducing spatial illusions. Second, an Asynchronous Closed-Loop Evaluator is implemented to compare pre- and post-execution states, providing text-based diagnostic feedback to establish a robust error-correction loop and improving the vulnerability of traditional open-loop execution in dynamic environments. Finally, we design a scalable multi-modal data engine that automatically synthesizes high-quality spatial annotations and reasoning templates from real and synthetic scenes without human teleoperation. Extensive experiments demonstrate that our approach significantly outperforms existing baselines, achieving an 87.0% overall success rate. Notably, the proposed framework exhibits remarkable generalization across diverse objects, bridging the sim-to-real gap and providing exceptional robustness in geometrically challenging categories and cluttered scenarios.
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。
