EAGER: Characterizing Physical Interaction in Instrument Manipulations

随着个人机器人在我们的家庭中变得普遍，它们将为它们的主人执行一系列有用的任务。在这样做的过程中，机器人和家庭环境之间将发生各种物理交互，例如，拿起杯子需要机器人在抓取时仔细控制接触力。随着任务变得更高级，例如操作开罐器，机器人将不得不“理解”如何抓住罐头和开罐器，这样它才能操作开罐器。该项目的目标是收集数据并开发算法，使机器人能够理解如何以一种有助于其安全有效地操作工具的方式来抓取工具。本研究旨在加深对日常操作作业中仪器与环境之间的物理相互作用的了解，目的是开发有效的机器人抓取和操作规划器，以促进作业的进行。本研究设计并开发了一个物理交互观察系统，用于观察多个参与者在几个具有代表性的仪器操作任务中仪器与环境之间的交互运动和扭力。每个操作任务都有其工具性运动模型和扳手分布模型。使用这些模型，使用任务扳手覆盖度量来生成最优抓取，并使用真实的机械臂和手平台进行评估。这项工作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良好的训练和测试数据集。最优操作抓取方法使机器人机械手能够牢牢握住仪器，抵御日常生活交互中的干扰，高效地执行任务。

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.