SCH: A Formalism for customizing and Training Intelligent Assistive Devices

SCH：定制和培训智能辅助设备的形式主义

• 批准号: 8919366
• 负责人: Brenna Argall
• 金额: $ 17.25万
• 依托单位: NORTHWESTERN UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-01 至 2017-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8919366
• 关键词: Activities of Daily Living; Address; Arbitration; Asses; Automation; Caregivers; Data; Dependence; Goals; Intelligence; Learning; Measures; Methods; Persons; Policies; Research; Robot; Robotics; Self-Help Devices; Signal Transduction; Solutions; Spinal cord injury patients; System; Testing; Time; Training; Upper Extremity; Work; arm; authority; base; improved; motor control; motor impairment; operation; preference

Highly capable assistive robotic arms are well-poised to dramatically increase the independence of those with severe motor impairments, by reducing their dependence on caregivers to perform select activities of daily living. However, the more sophisticated a robotic arm, the more complicated its control. Intuitive operation remains a challenge that only increases with task complexity, and is exasperated by limited or low-dimensional control interfaces. Our solution is to introduce machine automation and intelligence. We propose a formalism for customizable shared control that enables users to customize the way they share control with intelligent assistive devices based on the user's abilities and preferences. In our formalism, the system arbitrates between user input and the autonomous policy prediction, based on the confidence it has in the policy's prediction and in the user's ability to perform the task. Moreover, the system is invisible and able to augment minimal teleoperation interfaces (e.g. Sip-N-PufO- This point is critical for users whose own control signals are limited, and are only able to operate minimal interfaces as a result. While dexterous manipulation can be difficult for a user to achieve using minimal control interfaces, full robot autonomy is often lacking in robustness or unsatisfactory for users who wish to retain some control authority. Assistive teleoperation offers a customized and robust alternative. We propose methods for customizing system components based on user data that can generalize to new situations. In particular, we aim to address the following research questions: QI How can the system adapt its arbitration function to a new user or task? Q2 How can a user achieve the optimal arbitration function for a given task? Q3 How can the robot learn good policies from user demonstration and interaction? Q4 Are there user-centric and/or task-centric measures of confidence? We test the proposed methods for customizing system components in user studies with high Spinal Cord Injury patients as well as uninjured subjects. The result will be a system that can learn from its user and improve over time. The larger goal is assistive device-here specifically, robotic arm- operation that is accessible to, and intuitive for, persons with extremely limited or no upper limb motor control.

高性能的辅助机器人手臂已经做好了充分的准备，可以大大提高那些患有自闭症的人的独立性。 严重运动障碍，通过减少对护理人员进行日常选定活动的依赖 living.然而，机械臂越复杂，其控制就越复杂。直观操作 仍然是一个挑战，只会随着任务的复杂性而增加，并且由于有限或 低维控制接口。我们的解决方案是引入机器自动化和智能化。我们 提出了一种可定制的共享控件的形式，使用户能够定制他们共享的方式 根据用户的能力和偏好使用智能辅助设备进行控制。在我们的形式主义中， 系统基于其在以下方面具有的置信度在用户输入和自主策略预测之间进行仲裁： 策略的预测和用户执行任务的能力。而且，制度是看不见的，也是能做的 增强最小的远程操作界面（例如Sip-N-PufO-这一点对于自己的用户至关重要 控制信号是有限的，并且因此只能操作最小的接口。虽然灵巧 对于用户来说，使用最少的控制接口可能难以实现操纵，因此通常需要完全的机器人自主性。 对于希望保留某些控制权限的用户来说缺乏鲁棒性或不令人满意。辅助 远程操作提供了一种定制的和强大的替代方案。我们提出了定制系统的方法 基于用户数据的组件，可以推广到新的情况。特别是，我们的目标是解决 以下研究问题：QI系统如何使其仲裁功能适应新的用户或任务？ Q2用户如何实现给定任务的最佳仲裁功能？Q3机器人如何学习 好的政策来自用户的示范和互动？Q4是否有以用户和/或任务为中心的 信心措施？我们在用户研究中测试了所提出的定制系统组件的方法 高脊髓损伤患者以及未受伤的受试者。其结果将是一个可以学习的系统 并随着时间的推移而改善。更大的目标是辅助设备-这里具体来说，机器人手臂- 对于上肢运动控制极其有限或没有上肢运动控制的人来说，操作是可访问的，并且是直观的。