NRI: Collaborative Research: Human-Supervised Perception and Grasping in Clutter

NRI：协作研究：人类监督的杂乱中的感知和抓取

• 批准号: 1426968
• 负责人: Holly Yanco
• 金额: $ 49.62万
• 依托单位: University of Massachusetts Lowell
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-08-15 至 2019-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1426968&HistoricalAwards=false
• 关键词: NRI; Collaborative; Research; Human; Supervised

One of the basic building blocks in semi-autonomous manipulation is the ability for a robot to grasp an object that a human operator indicates. There are many tasks where the natural way for a human and robot to work together is for the human to point out the approximate locations of objects to be grasped and for the robot to generate the precise motions necessary to achieve the grasp. This core "auto-grasp" functionality is critical to providing assistive manipulation for the disabled and elderly, as well as for a variety of military, police, space, or underwater applications. But implementing auto-grasp capability can be challenging in situations where the environment is cluttered, or when it is difficult to determine the grasp intention of the human. In this collaborative project that combines expertise from two institutions, the PIs will tackle situations where it is necessary for the robot to actively explore or "interrogate" the environment in order to figure out what the human intends to grasp and how the robot should do it. To these ends, the PIs will investigate a modified approach to planning under uncertainty known as belief space planning. Belief space planning is well-suited to active localization for grasping, because it is a single framework in which the algorithm can reason about perception-oriented and goal-orientation parts of the task. The PIs will use belief space planning to localize graspable geometries in the environment, known as grasp affordances, in a region indicated by the user. They will also explore different ways in which a human can interact with the system in order to control the grasping. The application focus of the work will be in assistive manipulation, where a person who is elderly or disabled operates an assistive robot arm mounted on an electric wheelchair or scooter. User studies will determine the best methods for the target population to operate the system. The project will contribute to the opportunities available for undergraduates and high school students in the PIs' institutions, and it will also be integrated as appropriate into the curricula of the courses they teach.This research contains two key innovations that the PIs expect will make robot grasping more robust. The first is to incorporate ideas from belief space planning into the reach and grasp planning process. Because belief space planning can reason about how the robot's own "state of information" is expected to change in the future, it is capable of producing plans that acquire task-relevant information in the course of performing a task. The second innovation is a new approach to perception-for-grasping that localizes grasp affordance geometries in the neighborhoods of objects of potential interest. Not only is this grasp affordance approach helpful to the belief space planner, but the PIs' preliminary work indicates that this approach can be accurate and very fast (10Hz). Finally, the connection between the user interface and uncertainty in the location of the grasp target will also be explored, the plan being to model human behavior as an uncertain system where hidden variables describe user intention.

半自治操作中的基本构件之一是机器人掌握人类操作员指示的物体的能力。 在许多任务中，人类和机器人一起工作的自然方式是人类指出要抓住物体的近似位置，并供机器人产生实现掌握所需的精确动作。 这种核心“自动抓”功能对于为残疾人和老年人以及各种军事，警察，太空或水下应用提供辅助操作至关重要。但是，在环境混乱或难以确定人类的掌握意图的情况下，实施自动抓紧能力可能是具有挑战性的。 在将两个机构的专业知识结合起来的合作项目中，PI将解决机器人有必要积极探索或“询问”环境的情况，以找出人类打算掌握的内容以及机器人应该如何做到这一点。 对于这些目的，PI将调查一种在不确定性的信念空间规划下进行的修改方法。 信念空间规划非常适合主动掌握，因为它是一个单一的框架，算法可以推理任务的面向感知和目标方向的部分。 PI将使用信念空间计划在用户指示的区域中在环境中定位可抓地的几何形状，称为Grasp Provrice。 他们还将探索人类与系统相互作用以控制抓地力的不同方式。 这项工作的应用将集中在辅助操作上，在辅助操作中，老年人或残疾人的人操作一个安装在电动轮椅或踏板车上的辅助机器人手臂。 用户研究将确定目标人群操作系统的最佳方法。 该项目将为PIS机构中的大学生和高中生提供的机会做出贡献，并且还将适当地集成到他们所教的课程的课程中。这项研究包含了PIS期望的两项关键创新，PIS期望使机器人更加稳健。 首先是将信仰空间规划中的想法纳入覆盖范围并掌握计划过程。 因为信仰太空规划可以推理机器人自己的“信息状态”如何在将来发生变化，因此它能够制定计划在执行任务过程中获取与任务相关的信息的计划。 第二种创新是一种新的方法，可以在潜在感兴趣的物体附近掌握负担的几何形状。 这种掌握的负担方法不仅有助于信仰空间策划者，而且PIS的初步工作表明这种方法可以准确且非常快（10Hz）。 最后，还将探讨用户界面与不确定性之间的连接，也将探讨“掌握目标”位置，该计划是将人类行为建模为一个不确定的系统，其中隐藏变量描述了用户意图。