Human and Machine Learning for Customized Control of Assistive Robots

项目摘要 这一应用将产生一个技术平台，使严重瘫痪的人能够重新获得能力， 独立控制各种机器人动作。严重的瘫痪是毁灭性的，和慢性的-和依赖 对看护者的影响是持久的。轮椅和机器人手臂等辅助机器提供了一条突破性的道路， 独立性：将对环境和互动的控制权交还给个人。 然而，操作复杂的机器，如机器人手臂和手，通常会带来困难的学习挑战， 需要复杂的控制信号，以及严重瘫痪的人可以使用的商业控制接口 (e.g.抽吸、基于开关的头阵列）是不够的。因此，辅助机器人手臂在很大程度上仍然是 严重瘫痪的人无法获得，可以说是贝内最多的人群。 这项研究的目的是为四肢瘫痪的人提供控制机器人手臂的方法， 他们可用的身体活动性，同时促进可用的身体运动的锻炼和维护 身体健康。控制接口，其从用户的身体运动生成唯一的映射以控制信号， 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）的信号。我们提出 一种利用机器人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。具体来说，最初人类会发出较低的- 尺寸控制信号和机器人自主性用于通过接管未覆盖的任何内容来弥合差距， 人类的控制信号机器人自主性的帮助，然后逐步缩小，自动，以涵盖 随着用户变得更加熟练，控制尺寸越来越少。 我们的方法的第一部分涉及如何使用身体机器从人身上提取控制信号 接口.利用剩余体控制机器人手臂的解码程序的开发和优化 将在具体目标1下处理动议。我们方法的第二部分涉及如何解释控制 在人类和机器人自治之间共享控制的范例中，来自人类的信号。以识别 具体目标2的主题是，哪种共享控制公式最有效地利用了人类的控制信号。 我们的方法的最后一部分涉及如何调整共享控制范式，以便转移更多的控制权 对人类的影响这种适应对于人类的学习过程是必要的，因为最终的目标是为了 人类能够完全控制机器人手臂，并将根据具体目标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剩余身体操作机器人手臂 运动，通过既促进使用剩余身体运动（并且因此也恢复运动 随着时间的推移，他们的能力会随着时间的推移而变化。通过利用自适应机器人自主性，我们的 本申请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.