论文标题
多对象抓握 - 类型和分类学
Multi-Object Grasping -- Types and Taxonomy
论文作者
论文摘要
本文提出了来自人类和机器人抓地数据集的12种多对象grasps（MOGS）类型。然后将GRASP类型分析并组织为MOG分类学。本文首先介绍了三个MOG数据收集设置：用于多对象握把演示的人手指跟踪设置，带有Barretthand，UR5E ARM和MOG算法的真实系统，一种模拟系统，具有与真实系统相同的设置。然后，本文描述了一种基于有偏见的随机步行设计设计的新型随机抓紧程序，以探索机器人手的配置空间。基于人类示范和机器人MOG解决方案的观察结果，本文提出了两组的12种MOG类型：基于形状的类型和基于功能的类型。使用六个特征比较新的MOG类型，然后将其编译成分类法。然后，本文介绍了观察到的MOG类型组合，并显示了16种不同组合的示例。
This paper proposes 12 multi-object grasps (MOGs) types from a human and robot grasping data set. The grasp types are then analyzed and organized into a MOG taxonomy. This paper first presents three MOG data collection setups: a human finger tracking setup for multi-object grasping demonstrations, a real system with Barretthand, UR5e arm, and a MOG algorithm, a simulation system with the same settings as the real system. Then the paper describes a novel stochastic grasping routine designed based on a biased random walk to explore the robotic hand's configuration space for feasible MOGs. Based on observations in both the human demonstrations and robotic MOG solutions, this paper proposes 12 MOG types in two groups: shape-based types and function-based types. The new MOG types are compared using six characteristics and then compiled into a taxonomy. This paper then introduces the observed MOG type combinations and shows examples of 16 different combinations.
