CAREER: Haptic Interaction for Robotic Caregivers

职业：机器人护理人员的触觉交互

• 批准号: 1150157
• 负责人: Charles Kemp
• 金额: $ 50万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-03-01 至 2018-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1150157&HistoricalAwards=false
• 关键词: CAREER; Haptic; Interaction; Robotic; Caregivers

Making contact with a person's body is critical to many of the most important caregiving tasks for people with physical disabilities. During these tasks, the forces applied by the robot to the body of the human client (care recipient) are of central importance. Yet robots are currently ignorant of what forces are appropriate for common tasks, and what forces are appropriate when making contact with different locations on the client's body. In this project, the PI's goal is to endow assistive robots with the ability to use appropriate forces when haptically interacting with people. To this end, he will capture and statistically model the forces applied when a person performs assistive tasks for him or herself, or provides care to another person. He will enable robots to intelligently regulate the forces they apply when performing assistive tasks, so that the applied forces are comparable to those used during human-human interactions. And he will enable clients to effectively control the forces applied by a robot during assistive tasks. Throughout the research, the PI will conduct experiments to test relevant hypotheses: That the type of task and the pose of the tool relative to the client's body are highly predictive of the force applied by a human caregiver; That when performing tasks on a mannequin, the robot will successfully emulate the forces observed during human-human interaction; That when the robot applies force to the client's body, the client will prefer that the robot use knowledge of the task and the pose of the tool to interpret user commands rather than a constant mapping. Because a person's ability to perform activities of daily living (ADLs) is highly predictive of his or her ability to live independently, the work will focus on four representative ADL tasks that require contact with the client's head: feeding a person yogurt, wiping a person's face, brushing a person's hair, and shaving a person with an electric razor. Project outcomes will include a system that enables a PR2 robot from Willow Garage to assist people with severe physical disabilities with these four tasks; the PR2 will be modified to have force-torque sensors at its wrists, specialized tools, and a Kinect 3D sensor on its head.Broader Impacts: This research will begin to endow robots with a crucial form of "common sense" while quantitatively analyzing and synthesizing haptic interaction in the context of humans' most basic needs. It will also lead to a better understanding of human-robot interaction when the robot initiates contact with the user, and will contribute to data-driven methods for intelligent control. The PI will publish extensively and will release open source code, so that the work can catalyze progress towards robots that could empower millions of people to live more independently with a higher quality of life. The PI will directly involve people with disabilities in the research, and will actively engage the broader community by participating in events such as the RESNA conferences and the Atlanta Abilities Expo. In addition, he will incorporate research results in his biomechanics class and graduate course on haptics, and will then adapt the material to teach people around the world about these topics and robotics using the methods and tools of Khan Academy (http://www.khanacademy.org/).

与人的身体接触对于身体残疾人的许多最重要的工作任务至关重要。 在这些任务中，由机器人施加到人类客户（护理接受者）的身体上的力是至关重要的。 然而，机器人目前不知道什么力量适合于普通任务，以及什么力量适合于与客户身体上的不同位置接触。 在这个项目中，PI的目标是赋予辅助机器人在与人进行触觉交互时使用适当力量的能力。 为此，他将捕捉和统计建模的力量时，一个人为他或她自己执行辅助任务，或提供照顾另一个人。 他将使机器人能够智能地调节它们在执行辅助任务时所施加的力，以便所施加的力与人与人之间交互时所使用的力相当。 他将使客户能够有效地控制机器人在辅助任务中施加的力。 在整个研究过程中，PI将进行实验以测试相关假设：任务类型和工具相对于客户身体的姿势高度预测人类护理人员施加的力;当在人体模型上执行任务时，机器人将成功模仿在人与人互动期间观察到的力;当机器人向客户的身体施加力时，客户将更喜欢机器人使用任务的知识和工具的姿势来解释用户命令，而不是恒定的映射。 由于一个人进行日常生活活动（ADL）的能力高度预测他或她独立生活的能力，这项工作将集中在四个代表性的ADL任务，需要与客户的头部接触：喂一个人酸奶，擦一个人的脸，刷一个人的头发，用电动剃须刀刮一个人。 项目成果将包括一个系统，使杨柳Garage的PR 2机器人能够帮助严重身体残疾的人完成这四项任务; PR 2将被修改，在其手腕上安装力-扭矩传感器，专用工具和头部的Kinect 3D传感器。这项研究将开始赋予机器人一种至关重要的“常识”形式同时在人类最基本需求的上下文中定量分析和合成触觉交互。 当机器人开始与用户接触时，它还将导致更好地理解人机交互，并将有助于数据驱动的智能控制方法。 PI将广泛发布并发布开源代码，以便这项工作能够促进机器人的进步，使数百万人能够更独立地生活，生活质量更高。 PI将直接让残疾人参与研究，并通过参加RESNA会议和亚特兰大能力博览会等活动积极参与更广泛的社区。 此外，他将把研究成果纳入他的生物力学课程和触觉研究生课程，然后将改编材料，使用Khan Academy的方法和工具向世界各地的人们教授这些主题和机器人技术（http：//www.khanacademy.org/）。