NRI-Small: Collaborative Research: Assistive Robotics for Grasping and Manipulation using Novel Brain Computer Interfaces

这是一项合作提案（与加州大学戴维斯分校），旨在使辅助机器人的一些主要目标具体化。来自信号处理和控制、机器人抓取和康复医学领域的专家团队创建了一个可现场部署的辅助机器人系统，该系统将允许严重残疾患者控制机器人手臂/手系统，使用新型脑肌肉计算机接口（BMCI）执行复杂的抓取和操作任务。此外，这项工作的目的不仅仅是技术驱动，还受到明确和必要的临床需求的驱动，并将根据它如何满足这些临床需求进行评估。验证将在哥伦比亚大学再生和康复医学系对一组不同的残疾用户进行，这些用户将对正在开发的技术提供重要的反馈，这些反馈将用于系统设计和实现的迭代。智力优势：本提案的智力优势包括：o人机界面的新研究，有可能从简单和非侵入性的表面肌电图（sEMG）传感器中获得丰富的、多自由度的信号内容。o开发智能自适应软件，该软件采用机器学习算法，可以持续监控用户性能，然后根据系统性能自动校准和调整系统参数。o数据驱动的实时抓取规划算法，可用于已知和未知对象。o寻找可容忍传感误差的位姿鲁棒抓取点的方法。o评估欠驱动手作为某些应用任务的抓取装置。o将3D视觉与实时抓取规划相结合。o在临床一级对这些新技术对残疾人的影响进行科学评价。更广泛的影响：该提案的更广泛的影响包括：o开发一个完整的系统，以帮助患有四肢瘫痪的严重残疾人群。o将该技术扩展到其他缺乏运动控制功能的患者，包括多发性硬化症、中风、肌萎缩侧索硬化症（ALS或Lou Gehrig病）、脑瘫和肌肉萎缩症。o能够扩展辅助机器人领域的范围和影响的新技术。o开源GraspIt!的主要扩展软件系统，将允许许多其他研究人员利用这个项目的结果。o将工程专业学生、临床医生和残疾人士聚集在一起，扩大辅助机器人技术的范围和范围。o人机界面的新方向，可以扩展到残疾人群以外的各种其他应用。

This is a collaborative proposal (with UC Davis) which is aimed at making concrete some of the major goals of Assistive Robotics. A team of experts has been brought together from the fields of signal processing and control, robotic grasping, and rehabilitative medicine to create a field-deployable assistive robotic system that will allow severely disabled patients to control a robot arm/hand system to perform complex grasping and manipulation tasks using novel Brain Muscle Computer Interfaces (BMCI). Further, the intent of this effort is not just technology-driven, but is also driven by clear and necessary clinical needs, and will be evaluated on how well it meets these clinical requirements. Validation will be performed at the Department of Regenerative and Rehabilitation Medicine at Columbia University on a diverse set of disabled users who will provide important feedback on the technology being developed, and this feedback will be used to iterate on the system design and implementation.Intellectual Merit: The intellectual merit of this proposal includes:o Novel research in Human Machine Interfaces that has the potential to be transformative in eliciting rich, multi-degree-of-freedom signal content from simple and non-invasive surface electromyographic (sEMG) sensors.o Development of smart adaptive software that employs machine learning algorithms that can continually monitor user performance, and then automatically calibrate and tune system parameters based on system performance.o Data driven methods for real-time grasp planning algorithms that can be used with both known and unknown objects.o Methods for finding pose-robust grasps that are tolerant of errors in sensing.o Evaluation of an underactuated hand as a grasping device for certain application tasks.o Integration of 3D vision with real-time grasp planning.o Scientific evaluation at the clinical level of the impact of these new technologies on the disabled population.Broader Impacts: The broader impacts of this proposal include:o Development of a complete system to aid the severely disabled population with tetraplegia.o Extensions of this technlogy to others lacking motor control function including multiple sclerosis, stroke, amyotrophic lateral sclerosis (ALS or Lou Gehrig disease), cerebral palsy, and muscular dystrophy.o New technology that can extend the reach and impact of the field of Assistive Robotics.o Major extensions to the open-source GraspIt! software system that will allow many other researchers to leverage the results of this project.o Educational thrusts that will bring together engineering students, clinicians and the disabled population to extend the reach and scope of Assistive Robotics.o New directions in Human Machine Interfaces that can extend beyond the disabled population and into a variety of other applications.