SCH: A Formalism for customizing and Training Intelligent Assistive Devices

高性能的辅助机械臂已经做好了准备，可以极大地提高那些有 严重的运动障碍，通过减少他们对照顾者执行日常选定活动的依赖 活着。然而，机械臂越复杂，其控制就越复杂。操作直观 仍然是一个挑战，只会随着任务的复杂程度而增加，并因有限的或 低维控制界面。我们的解决方案是引入机器自动化和智能化。我们 为可定制的共享控件提出一种形式，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。在形式主义中， 系统根据其对以下各项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。此外，该系统是不可见的，并且能够 为了增加最小的远程操作接口(例如，sip-N-pufo--这一点对于其自身的用户来说是至关重要的 控制信号是有限的，因此只能操作最小的接口。灵巧的同时 对于用户来说，使用最少的控制界面很难实现操作，完全的机器人自主性通常是 对于希望保留一些控制权限的用户来说，缺乏健壮性或不能令人满意。助教 远程操作提供了一种定制的、强大的替代方案。提出了对系统进行定制的方法 基于可概括为新情况的用户数据的组件。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用户如何才能实现给定任务的最优仲裁功能？问题3：机器人如何学习？ 来自用户演示和互动的好策略？第4季度是否以用户为中心和/或以任务为中心 信心的衡量标准？我们在用户研究中测试了所提出的定制系统组件的方法 高位脊髓损伤患者以及未损伤的受试者。其结果将是一个可以学习的系统 并随着时间的推移不断改进。更大的目标是辅助设备--具体地说，是机械臂-- 对于上肢运动控制极其有限或没有的人来说，这是一种可及且直观的操作。

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.