NRI: FND: Collaborative Control for Wearable Robots

有许多应用，包括制造、装配、医疗保健和建筑，工人可能会因为没有足够的手来有效地完成工作而受到阻碍。虽然有一种方法是让移动机器人辅助人类，但我们的项目侧重于通过开发可穿戴机械臂来增强人类的能力。人体的这种增强可以增强人的力量、效率、安全和工作质量。该项目旨在使这些可穿戴机器人充当协作队友，而不是直接控制的被动工具，使它们既适合新手，又适合专业用户。这将显著提高这类机器人的效率和接受度，并对与它们互动的人的工作条件产生积极影响。机器人手臂将在人类用户中进行测试，其中第三只手臂是有用的。虽然可穿戴机器人是一个成熟的领域，但几乎没有关于这种机器人的人机交互（HRI）方面的研究。本项目研究可穿戴机器人的协作HRI，分为四个阶段：1)收集与人类和远程操作机械臂的可穿戴协作数据；2)利用该数据设计合作的预期条件随机场（CRF）模型；3)开发了机器人部分可观察马尔可夫决策过程（POMDP）控制器；4)使用效率、流畅性和可用性指标，对物理可穿戴机械臂人机交互实验中的控制器进行评估。通过这样做，该项目通过开发人类可穿戴机器人协作的新概率模型，为计算HRI的最新技术做出了贡献。该项目还通过两项人体研究，为人们如何与可穿戴协作机器人互动提供了新的经验数据。收集到的关于人-可穿戴-机器人交互的数据集将被发布，以服务于研究界。

There are many applications, including manufacturing, assembly, health care, and construction, where workers may be hampered by not having enough hands to do their job effectively. While one approach is to have a mobile robot assist the human, our project instead focuses on the augmenting human capabilities by developing a wearable robotic arm. Such augmentation of the human body can enhance a person's power, efficiency, safety, and quality of work. The project aims to make these wearable robots act as collaborative teammates, rather than directly controlled passive tools, making them both intuitive for novices and adaptable to expert users. This will significantly improve the efficiency and acceptance of such robots, and positively affect the work conditions of people interacting with them. The robot arm will be tested with human users performing tasks for which a third arm is useful. Whereas wearable robotics is a maturing field, there is almost no research on the human-robot interaction (HRI) aspects of such robots. This project investigates collaborative HRI for wearable robots, in four phases: 1) Collecting wearable collaboration data with a human and a tele-operated robot arm; 2) using this data to design an anticipatory Conditional Random Field (CRF) model for the collaboration; 3) developing a Partially Observable Markov Decision Process (POMDP) controller for the robot; and 4) evaluating the controller in human-robot interaction experiments with a physical wearable robotic arm, using metrics of efficiency, fluency, and usability. By doing so, the project contributes to the state-of-the-art in computational HRI by developing new probabilistic models for human-wearable robotic collaboration. The project also contributes new empirical data on how people interact with wearable co-robots through two human-subject studies. The collected data set on human-wearable-robotic interaction will be released to serve the research community.