Human and Machine Learning for Customized Control of Assistive Robots

项目总结 这项应用将产生一个技术平台，通过允许严重瘫痪患者 以独立控制广泛的机器人动作。严重的瘫痪是毁灭性的，而且是慢性的-依赖 对照顾者的影响是持久的。轮椅和机械臂等辅助机器提供了一条开创性的道路 独立性：将对环境和互动的控制权交还给个人。 然而，操作像机械臂和手这样的复杂机器通常会构成一个很大的fi邪教学习挑战和 需要复杂的控制信号-以及严重瘫痪患者可访问的商业控制界面 (例如，sip-and-puff、基于开关的磁头阵列)是不够的。因此，辅助机械臂在很大程度上仍然存在 对于那些严重瘫痪的人来说是无法接触到的--可以说是最能让fi受益的人群。 这项拟议的研究的目的是为四肢瘫痪患者提供控制机械臂的手段 他们的可用身体活动能力，同时促进可用身体运动的锻炼和维持 身体健康。控制接口，其根据用户的身体运动生成唯一的地图以控制信号 机器提供定制的交互，然而这些接口仅用于发布低维 (2-D)控制信号，而更复杂的机器需要更高维(例如6-D)信号。我们建议 一种利用机器人技术自主性和机器学习的方法，以帮助最终用户学习如何 通过身体运动发出有效的高维控制信号。特别是fiCally，最初人类发布了一个更低的- 空间控制信号和机器人自主性被用来通过接管任何没有覆盖的东西来弥合差距 人类的控制信号。然后，来自机器人自主性的帮助被逐步缩减，自动地覆盖 随着用户变得越来越熟练，控制维度越来越少。 我们方法的第一篇fi文章涉及如何使用人体机器从人体提取控制信号 界面。利用残余体控制机械臂的译码程序的开发与优化 动议将在规范fic目标1下处理。我们方法的第二部分涉及如何解释控制 在人类和机器人自主之间共享控制权的范例中，来自人类的信号。要确定 哪些共享控制配方最有效地利用了人类的控制信号，这将是SPECIfic Aim 2的主题。 我们方法的fiNAL文章涉及如何适应共享控制范例，以便转移更多的控制权 随着时间的推移对人类的影响。这种适应对于人类的学习过程是必要的，因为最终的目标是 人类能够完全控制他/她自己的机械臂，并将根据特殊fic目标3进行评估。 在这个项目完成后，四肢瘫痪的最终用户将能够使用他们的残肢来操作机械臂 运动，通过一个接口，既促进了身体残余运动的使用(也促进了运动的恢复 技能)，并随着人类的能力随着时间的推移而改变。通过利用自适应机器人自主，我们的 此外，应用程序还提供了一种安全机制，以便于学习如何操作机器人平台。

PROJECT SUMMARY This application will result in a technological platform that re-empowers persons with severe paralysis, by allowing them to independently control a wide spectrum of robotic actions. Severe paralysis is devastating, and chronic—and reliance on caregivers is persistent. Assistive machines such as wheelchairs and robotic arms offer a groundbreaking path to independence: where control over their environment and interactions is returned to the person. However, to operate complex machines like robotic arms and hands typically poses a difficult learning challenge and requires complex control signals—and the commercial control interfaces accessible to persons with severe paralysis (e.g. sip-and-puff, switch-based head arrays) are not adequate. As a result, assistive robotic arms remain largely inaccessible to those with severe paralysis—arguably the population who would benefit from them most. The purpose of the proposed study is to provide people with tetraplegia with the means to control robotic arms with their available body mobility, while concurrently promoting the exercise of available body motions and the maintenance of physical health. Control interfaces that generate a unique map from a user's body motions to control signals for a machine offer a customized interaction, however these interfaces have only been used to issue low-dimensional (2-D) control signals whereas more complex machines require higher-dimensional (e.g. 6-D) signals. We propose an approach that leverages robotics autonomy and machine learning in order to aid the end-user in learning how to issue effective higher-dimensional control signals through body motions. Specifically, initially the human issues a lower- dimensional control signal and robotics autonomy is used to bridge the gap by taking over whatever is not covered by the human's control signal. Help from the robotics autonomy is then progressively scaled back, automatically, to cover fewer and fewer control dimensions as the user becomes more skilled. The first piece to our approach deals with how to extract control signals from the human, using the body-machine interface. The development and optimization of decoding procedures for controlling a robotic arm using residual body motions will be addressed under Specific Aim 1. The second piece to our approach deals with how to interpret control signals from a human within a paradigm that shares control between the human and robotics autonomy. To identify which shared-control formulations most effectively utilize the human's control signals will be the topic of Specific Aim 2. The final piece to our approach deals with how to adapt the shared-control paradigm so that more control is transferred to the human over time. This adaptation is necessary for the human's learning process, since the goal in the end is for the human to be able to fully control the robotic arm him/herself, and will be assessed under Specific Aim 3. At the completion of this project, tetraplegic end-users will be able to operate a robotic arm using their residual body motions, through an interface that both promotes the use of residual body motions (and thus also recovery of motor skill) and adapts with the human as their abilities change over time. By leveraging adaptive robotics autonomy, our application moreover provides a safe mechanism to facilitate learning how to operate the robotic platform.