EAGER: Characterizing Physical Interaction in Instrument Manipulations

EAGER：表征仪器操作中的物理交互

• 批准号: 1560761
• 负责人: Yu Sun
• 金额: $ 29.99万
• 依托单位: University of South Florida
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-03-01 至 2019-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1560761&HistoricalAwards=false
• 关键词: EAGER; Characterizing; Physical; Interaction; Instrument

As personal robots become common in our homes, they will perform a broad range of helpful tasks for their owners. In doing so, all sorts of physical interactions will occur between the robot and the home environment, for example, picking up a mug requires the robot to carefully control contact forces as it secures a grasp. As tasks become more advanced, for example, operating a can opener, the robot will have to "understand" how to grasp the can and the can opener, so that it can operate the opener. The goal of this project is to gather data and develop algorithms that will allow a robot to understand how to grasp tools in a way that facilitates its ability to operate that tool safely and effectively. This research aims to improve the understanding of the physical interactions between the instruments and the environment in daily manipulation tasks with the goal of developing effective robotic grasp and manipulation planners to facilitate the tasks. The research designs and develops a physical-interaction observation system to observe both the interactive motion and wrench between the instruments and environment in several representative instrumental manipulation tasks by a number of participants. Each manipulation task is characterized with its instrumental motion models and wrench distribution models. Using the models, optimal grasps are generated using the task wrench coverage measure and evaluated using a real robotic arm and hand platform. The data collected in this work enables researchers to fully explore daily living tasks and provide excellent training and testing data sets for other related research. The optimal manipulation grasping approach equips robot manipulators with the ability to hold instruments with a firm grasp to withstand the disturbance in daily living interactions and perform tasks efficiently.

随着个人机器人在我们的家中变得普遍，他们将为所有者执行一系列有用的任务。 这样做，例如，机器人和家庭环境之间将发生各种物理互动，例如，拿起杯子需要机器人在确保掌握时仔细控制接触力。随着任务变得越来越高级，例如操作罐头开门器，机器人将必须“了解”如何掌握罐子和开瓶器，以便它可以操作开口机。 该项目的目的是收集数据并开发算法，这些算法将使机器人能够以促进其安全有效地操作该工具的能力的方式了解如何掌握工具。这项研究旨在在日常操纵任务中提高对仪器与环境之间物理相互作用的理解，目的是开发有效的机器人掌握和操纵计划者以促进任务。该研究设计并开发了一个物理相互作用观察系统，以观察许多参与者在几种代表性的仪器操纵任务中仪器和环境之间的互动运动和扳手。每个操作任务都以其仪器运动模型和扳手分配模型来表征。使用这些模型，使用任务扳手覆盖量措施生成了最佳掌握，并使用真正的机器人手臂和手动平台进行了评估。这项工作中收集的数据使研究人员能够充分探索日常生活任务，并为其他相关研究提供出色的培训和测试数据集。 最佳的操纵握把方法使机器人操纵器具有牢固掌握的仪器的能力，以承受日常生活互动中的干扰并有效地执行任务。