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CAREER: Reinforcement-Learning Assist-As-Needed Control For Robot-Assisted Gait Training

职业：机器人辅助步态训练的强化学习辅助按需控制

基本信息

批准号：
1944203
负责人：
Damiano Zanotto
金额：
$ 59.75万
依托单位：
Stevens Institute of Technology
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-05-01 至 2025-04-30
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1944203&HistoricalAwards=false
关键词：
CAREER Reinforcement Learning Assist Needed

项目摘要

This Faculty Early Career Development (CAREER) grant will develop adaptive, assist-as-needed controllers for a powered ankle brace (orthosis). Such systems may promote the learning of desired gait patterns during physical rehabilitation. Reinforcement learning will be used to shape person-specific control policies that balance movement error and user effort. The approach accounts for each user’s ability to learn and their existing patterns of inter-limb coordination. The control methods will be tested by people walking on a treadmill at constant speed, and over-ground at self-selected speeds. Research participants will include healthy people and a small group of stroke survivors with gait deficits. This project will promote the progress of science and advance the national health by developing a new intelligent ankle-foot orthosis controller. This new controller promises to improve gait retraining outcomes in stroke survivors. The project includes a plan to advance engineering education. It will also spread knowledge of wearable robots at a STEM camp offered to underrepresented middle school children.This project will develop novel assist-as-needed control methods for powered ankle orthoses. There are two main research objectives in this project. The first will establish new reinforcement learning (RL-based) control strategies capable of self-adapting the control policy of a robotic orthosis to optimally balance the tradeoff between movement error and user effort in order to promote human learning of target gait trajectories. The performance of four different RL-based controllers will be compared to that of the most common form of adaptive assist-as-needed controller during treadmill walking by neurologically intact individuals and by a small cohort of stroke survivors. The second objective will extend these novel control strategies to situations wherein the desired target motion is unknown to the controller and must be computed on-line. This step is necessary in order to encourage desired patterns of interlimb coordination during over-ground walking, where terrain can be uneven and obstacles are to be avoided. Both objectives will be pursued by investigating model-free on-line RL control methods for optimal policy search, using adaptive frequency oscillators coupled with kernel-based nonlinear filters for on-line estimation of desired, time-varying gait trajectories. The research promises to advance a fundamental understanding of the bidirectional adaptations that can arise when adaptive human and machine intelligences interact through physical channels, here in the context of gait rehabilitation.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.

这项教师早期职业发展（Career）资助将为动力踝关节支架（矫形器）开发自适应的，根据需要的辅助控制器。这样的系统可以促进身体康复过程中所需步态模式的学习。强化学习将用于制定个人特定的控制策略，以平衡运动误差和用户努力。该方法考虑了每个用户的学习能力和他们现有的肢体间协调模式。这些控制方法将通过人们在跑步机上以恒定速度行走和在地面上以自行选择的速度行走来测试。研究参与者将包括健康的人和一小群有步态缺陷的中风幸存者。本项目开发一种新型智能踝足矫形器控制器，将促进科学进步，促进国民健康。这种新的控制器有望改善中风幸存者的步态再训练效果。该项目包括一项推进工程教育的计划。该公司还将在一个面向代表性不足的中学生的STEM夏令营中传播可穿戴机器人的知识。该项目将为动力踝关节矫形器开发新的按需辅助控制方法。这个项目有两个主要的研究目标。首先将建立新的基于强化学习（rl）的控制策略，该策略能够自适应机器人矫形器的控制策略，以最佳地平衡运动误差和用户努力之间的权衡，以促进人类对目标步态轨迹的学习。四种不同的基于rl的控制器的性能将与最常见的自适应辅助控制器在跑步机上行走时的性能进行比较，这些控制器由神经系统完好的个体和一小群中风幸存者组成。第二个目标是将这些新的控制策略扩展到控制器未知目标运动且必须在线计算的情况。这一步是必要的，以便在地面上行走时促进所需的四肢间协调模式，因为地面上的地形可能不平坦，需要避开障碍物。这两个目标将通过研究无模型在线强化学习控制方法来实现最优策略搜索，使用自适应频率振荡器和基于核的非线性滤波器来在线估计期望的时变步态轨迹。这项研究有望推进对双向适应的基本理解，当适应性人类和机器智能通过物理通道相互作用时，就会出现双向适应，这里是在步态康复的背景下。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

期刊论文数量（5）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

CyberCoach: a Wearable Biofeedback System for Runners

CyberCoach：跑步者可穿戴生物反馈系统

DOI：
发表时间：
2022
期刊：
Proceedings of the IEEERASEMBS International Conference on Biomedical Robotics and Biomechatronics
影响因子：
0
作者：
Gibson, Matthew R.;Boergers, Richard J.;Zanotto, Damiano
通讯作者：
Zanotto, Damiano

Shaping Individualized Impedance Landscapes for Gait Training via Reinforcement Learning

DOI：
10.1109/tmrb.2021.3137971
发表时间：
2021-09
期刊：
IEEE Transactions on Medical Robotics and Bionics
影响因子：
0
作者：
Yufeng Zhang;Shuai Li;Karen J. Nolan;D. Zanotto
通讯作者：
Yufeng Zhang;Shuai Li;Karen J. Nolan;D. Zanotto

Reinforcement Learning Assist-as-needed Control for Robot Assisted Gait Training

机器人辅助步态训练的强化学习按需辅助控制

DOI：
10.1109/biorob49111.2020.9224392
发表时间：
2020
期刊：
2020 8th IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics (BioRob
影响因子：
0
作者：
Zhang, Yufeng;Li, Shuai;Nolan, Karen J.;Zanotto, Damiano
通讯作者：
Zanotto, Damiano

Efficient Digital Modeling and Fabrication Workflow for Individualized Ankle Exoskeletons

个性化踝外骨骼的高效数字建模和制造工作流程

DOI：
10.1115/imece2021-70603
发表时间：
2022
期刊：
Proceedings of the ASME 2021 International Mechanical Engineering Congress and Exposition
影响因子：
0
作者：
Gebre, Biruk A.;Nogueira, Rodrigo;Patidar, Shubham;Belle-Isle, Robert;Nolan, Karen J.;Pochiraju, Kishore;Zanotto, Damiano
通讯作者：
Zanotto, Damiano

Reinforcement Learning-Based Adaptive Biofeedback Engine for Overground Walking Speed Training