A Framework for Semi-Autonomous In-Hand Telemanipulation

该奖项支持研究开发和测试稳定的手持遥控操作的控制框架，使用物理信息机器学习方法，可以使遥控机器人控制器适应个人用户的生理限制。在日常生活中，需要手操作来完成看似简单的任务，例如调整电源适配器的方向以插入插座，以及完成复杂的技能任务，例如在手术过程中操纵手术刀。该项目启动的研究路线的长期目标是在远程操作场景中实现灵巧的手操作，其中机器人可以了解如何跟踪人类手指运动的实时变化，并使用该信息来积极确保手物体的稳定性。该项目通过开发和测试在远程手术，医疗辅助机器人和远程搜索和救援等远程操作场景中实现手持物体的稳定共享人机控制的新方法，促进科学进步，促进国民健康，繁荣和福利。 该项目还将通过科罗拉多矿业学院、当地社区学院、该项目旨在通过开发和测试一个控制框架来解决与手持遥控操作相关的挑战，该框架利用了一个可编程控制器，信息化的分层机器学习方法，以使遥控机器人适应个体用户的生理限制。研究目标有三个：1）利用物理信息指标指导机器人控制策略以产生稳定的抓取配置; 2）利用分层学习模型优化人手运动跟踪系统的信号解释;和3）通过由用户的校正调整引导的主动机器学习来个性化人类和机器人之间的操纵任务的共享控制实时的。该项目团队将使用一个商用机器人手系统与半自主框架进行人类受试者参与的评估。受试者将执行越来越复杂的远程操作任务，范围从相对简单的罐子打开任务到更复杂的情况下，需要在手的工具位置和方向的变化。这项工作有望通过一种新型的半自主控制框架来推进人机合作的科学和工程，该框架可以支持复杂的手持物体操作以实现远程操作。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

This award supports research to develop and test a control framework for stable in-hand telemanipulation, using a physic-informed machine learning approach that can adapt the telerobotic controller to physiological limitations of individual users. In daily life, in-hand manipulation is needed to complete seemingly simple tasks such as adjusting the orientation of a power adaptor for plugging in to an outlet, as well as to complete complex skilled tasks such as manipulating a scalpel during a surgical procedure. The long-term objective of the line of research initiated with this project is to enable dexterous in-hand manipulation in tele-operation scenarios in which the robot can understand how to track real-time changes in human finger motions and use that information to actively ensure the stability of an in-hand object while it is being manipulated. This project promotes the progress of science and advances the national health, prosperity, and welfare by developing and testing novel methods implementing stable shared human and machine control of hand-held objects in tele-operation scenarios, such as tele-surgery, healthcare assistive robotics, and remote search and rescue. The project will also support outreach activities through an existing K-12 program at the Colorado School of Mines, at a local community college, and at STEM camp for girls.This project seeks to solve challenges associated with in-hand telemanipulation through the development and testing of a control framework that utilizes physic-informed hierarchical machine learning approaches to adapt the telerobot to the physiological limitations of the individual users. There are three research objectives: 1) to use physics-informed metrics to guide the robot control policy to generate stable grasp configurations; 2) to optimize interpretation of signals derived from the human hand motion tracking system using a hierarchical learning model; and 3) to personalize shared control of the manipulation task between the human and the robot through active machine learning guided by the user's corrective adjustments in real-time. The project team will use a commercially available robotic hand system with the semi-autonomous framework to conduct human-subject-involved evaluation. Subjects will perform tele-manipulation tasks of increasing complexity, ranging from the relative simplicity of a jar opening task to a more complex case requiring in-hand changes of tool position and orientation. The work promises to advance the science and engineering of human-robot cooperation through a novel semi-autonomous control framework that can support complex in-hand object manipulation for teleoperation.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.