面向目标的对象抓取：基于多模态人类引导
Target-Oriented Object Grasping via Multimodal Human Guidance
AI总结：
针对传统抓取检测冗余低效问题，提出面向目标的抓取网络TOGNet，结合多模态人类引导，在杂乱场景中实现约13.7%的成功率提升。
摘要：
在人机交互与协作场景中，机器人抓取仍面临诸多挑战。传统抓取检测方法通常分析整个场景以预测抓取，导致冗余和低效。在本工作中，我们从目标引用的视角重新审视6自由度（6-DoF）抓取检测，并提出一种面向目标的抓取网络（TOGNet）。TOGNet专门针对局部、与对象无关的区域块，以更高效地预测抓取。它与多模态人类引导无缝集成，包括语言指令、指向手势和交互式点击。因此，我们的系统包含两个主要功能模块：一个引导模块，用于在3D空间中识别目标对象；以及TOGNet，用于检测目标周围的区域聚焦6-DoF抓取，从而促进后续的运动规划。通过在杂乱场景中进行的50次目标抓取仿真实验，我们的系统实现了约13.7%的成功率提升。在真实世界实验中，我们证明了我们的方法在各种面向目标的抓取场景中表现出色。
英文摘要：
In the context of human-robot interaction and collaboration scenarios, robotic grasping still encounters numerous challenges. Traditional grasp detection methods generally analyze the entire scene to predict grasps, leading to redundancy and inefficiency. In this work, we reconsider 6-DoF grasp detection from a target-referenced perspective and propose a Target-Oriented Grasp Network (TOGNet). TOGNet specifically targets local, object-agnostic region patches to predict grasps more efficiently. It integrates seamlessly with multimodal human guidance, including language instructions, pointing gestures, and interactive clicks. Thus our system comprises two primary functional modules: a guidance module that identifies the target object in 3D space and TOGNet, which detects region-focal 6-DoF grasps around the target, facilitating subsequent motion planning. Through 50 target-grasping simulation experiments in cluttered scenes, our system achieves a success rate improvement of about 13.7%. In real-world experiments, we demonstrate that our method excels in various target-oriented grasping 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。
