CHS: Small: Empowerment of Disabled Individuals via an Adaptive Framework for Indirect Human-Robot Interaction

CHS：小：通过间接人机交互的自适应框架为残疾人赋权

• 批准号: 1527794
• 负责人: Aman Behal
• 金额: $ 49.99万
• 依托单位: The University of Central Florida Board of Trustees
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2022-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1527794&HistoricalAwards=false
• 关键词: CHS; Small; Empowerment; Disabled; Individuals

Thanks to advances in autonomy, an increasing variety of robotic devices have emerged over the last few years to assist disabled users with mobility and object manipulation. However, users often report higher satisfaction from controlling and interacting with a robot, even when an autonomous robot shows better quantitative performance, because they tend to see the robot not merely as an agent for retrieving objects but as a quintessential tool for reasserting their domain of interaction with their environment and engaging their available faculties to the fullest. Sadly, for these users effective interaction with a robotic device is frequently hindered by the fact that haptic feedback in the normal sense may not be possible, whether due to sensory and/or cognitive disabilities or to limitations imposed by motor disabilities that may prevent proper transfer of user intent in the absence of an appropriate user interface. Motivated by these considerations, and in the hope of boosting the current low rates of assistive technology adoption by its intended users, the PI will in this project create a software framework that is independent of the robotic system and allows for adaptively compensating the level and type of human-robot interaction to increase user satisfaction based on real-time measurements of user performance. Project outcomes will foster a new paradigm for the design of assistive technology, which will enable system developers to understand the impact of user preferences and incorporate them in their designs from the start, as opposed to the current inefficient practice of user testing a design after creating an expensive product or prototype. This research will push the envelope of human-robot interaction through the creation of models for understanding the underlying intent of users with disabilities, which may adversely affect their environmental perception and response. By utilizing these empirical models to design an adaptive human-robot interface that can compensate for deficits and variability in user performance, the work will generate a novel framework for effective sharing of control between individuals with disabilities and their robotic assistants. Furthermore, the control design for physical human-robot interaction that will be developed as part of this work will advance the field of autonomous robotics in general through the creation of new algorithms for physically interacting with users and their environments. The research tasks include systematic modeling of user performance during human-robot interaction, estimation of user performance parameters within a generalized estimation framework, and design of adaptive Lyapunov-based control strategies to facilitate safe and efficient physical interaction of the robotic end-effector with the user and environmental objects. The work will be informed by quantitative/qualitative data to be gathered from extensive user studies in the field, by the PI's previous experience in working with this class of users, and by his expertise in assistive robotics.

由于在自主性方面的进步，在过去的几年里出现了越来越多的机器人设备，以帮助残疾用户进行移动和对象操作。然而，用户经常报告从控制和与机器人交互中获得更高的满意度，即使自主机器人表现出更好的量化性能，因为他们倾向于将机器人不仅视为取回对象的代理，而且将其视为重新确立其与环境交互的领域并最大限度地发挥其可用能力的典型工具。遗憾的是，对于这些用户来说，与机器人设备的有效交互经常受到以下事实的阻碍，即正常意义上的触觉反馈可能是不可能的，无论是由于感觉和/或认知障碍，还是由于运动障碍施加的限制，其可能在没有适当的用户界面的情况下阻止用户意图的适当传递。在这些考虑的推动下，并希望提高其预期用户目前较低的辅助技术采用率，PI将在该项目中创建一个独立于机器人系统的软件框架，并允许根据对用户表现的实时测量来自适应地补偿人-机器人交互的水平和类型，以提高用户满意度。项目成果将促进辅助技术设计的新范式，这将使系统开发人员能够了解用户偏好的影响，并从一开始就将其纳入他们的设计，而不是目前低效的做法，即用户在创建昂贵的产品或原型后测试设计。这项研究将通过创建模型来理解残疾用户的潜在意图，从而推动人与机器人交互的极限，这可能会对他们的环境感知和反应产生不利影响。通过利用这些经验模型来设计一个自适应的人-机器人界面，可以弥补用户性能的缺陷和可变性，这项工作将产生一个新的框架，在残疾人和他们的机器人助手之间有效地分享控制。此外，将作为这项工作的一部分开发的人-机器人物理交互控制设计将通过创建用于与用户及其环境进行物理交互的新算法来推动自主机器人领域的总体发展。研究任务包括人-机器人交互过程中用户行为的系统建模，在广义估计框架内估计用户行为参数，以及设计基于李亚普诺夫的自适应控制策略，以促进机器人末端执行器与用户和环境对象的安全和高效的物理交互。这项工作将通过从现场广泛的用户研究中收集的定量/定性数据、PI以前与这类用户合作的经验以及他在辅助机器人方面的专业知识来提供信息。