抓取你想要的：由你的声音驱动的具身灵巧抓取系统
Grasp What You Want: Embodied Dexterous Grasping System Driven by Your Voice
AI总结：
提出具身灵巧抓取系统EDGS，融合语音与视觉信息，实现杂乱环境下的精准物体抓取，实验验证高成功率。
摘要：
近年来，随着机器人技术的进步，人机协作的重要性日益凸显。然而，当前的机器人难以仅凭语音指令完全且准确地理解人类意图。传统的夹爪和吸盘系统往往无法与人类自然交互，缺乏高级操作能力，并且难以适应多样化任务，尤其是在非结构化环境中。本文介绍了具身灵巧抓取系统（EDGS），旨在解决人机交互中杂乱环境下的物体抓取问题。我们提出了一种新颖的语义-物体对齐方法，使用视觉语言模型（VLM）融合语音指令和视觉信息，显著增强了复杂场景中目标物体多维属性的对齐能力。受人类手-物体交互的启发，我们开发了一种稳健、精确且高效的抓取策略，融合了拇指-物体轴、多指包裹以及指尖与物体接触力学等原理。我们还设计了实验来评估指称表达表示增强（RERE）在指称表达分割中的表现，证明我们的系统能够准确检测和匹配指称表达。大量实验证实，EDGS能够有效处理复杂的抓取任务，实现稳定性和高成功率，突显了其在具身人工智能领域进一步发展的潜力。
英文摘要：
In recent years, as robotics has advanced, human-robot collaboration has gained increasing importance. However, current robots struggle to fully and accurately interpret human intentions from voice commands alone. Traditional gripper and suction systems often fail to interact naturally with humans, lack advanced manipulation capabilities, and are not adaptable to diverse tasks, especially in unstructured environments. This paper introduces the Embodied Dexterous Grasping System (EDGS), designed to tackle object grasping in cluttered environments for human-robot interaction. We propose a novel approach to semantic-object alignment using a Vision-Language Model (VLM) that fuses voice commands and visual information, significantly enhancing the alignment of multi-dimensional attributes of target objects in complex scenarios. Inspired by human hand-object interactions, we develop a robust, precise, and efficient grasping strategy, incorporating principles like the thumb-object axis, multi-finger wrapping, and fingertip interaction with an object's contact mechanics. We also design experiments to assess Referring Expression Representation Enrichment (RERE) in referring expression segmentation, demonstrating that our system accurately detects and matches referring expressions. Extensive experiments confirm that EDGS can effectively handle complex grasping tasks, achieving stability and high success rates, highlighting its potential for further development in the field of Embodied AI.
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。
