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CAREER: Human/Machine Collaborative Learning and Control of Contact-Rich Dynamics

职业：人/机协作学习和接触丰富的动力学控制

基本信息

批准号：
2045014
负责人：
Sam Burden
金额：
$ 73.67万
依托单位：
University of Washington
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2021
资助国家：
美国
起止时间：
2021-08-15 至 2027-04-30
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2045014&HistoricalAwards=false
关键词：
CAREER Human Machine Collaborative Learning

项目摘要

This Faculty Early Career Development (CAREER) grant seeks to discover fundamental engineering principles that enable humans and machines to collaboratively learn and control the complex dynamics that arise when the human or machine intermittently contacts objects or uneven terrain in the environment. Although humans successfully learn to control complex machine dynamics when they fly planes, drive cars, or perform robot-assisted surgery, these applications avoid the abrupt changes in dynamics encountered when one's feet hit the ground, or when one's hands initially grasp objects. Despite the prevalence of contact-rich dynamic interactions in daily life and work, state-of-the-art robots struggle with making and breaking contact whether or not there is a human in-the-loop. In many applications of current and future interest -- remotely-operated robots, active prosthetics and exoskeletons, and brain/machine interfaces, to name a few -- humans and machines will collaboratively learn to control legs and arms as they make and break contact with the environment. The project will advance the NSF mission to promote the progress of science and to advance national health, prosperity, and welfare by advancing understanding of how humans learn to control contact-rich dynamics and how machines can adapt to ensure safety and improve performance of the human/machine system. In the long term, the results of this project will human/robot teams to perform complex tasks involving dynamic interaction with the world. Since many jobs are becoming increasingly automated, and since many people will experience impaired movement at some point in their lives, the goals of this research have tangible benefits for work and/or health of many people. To ensure these benefits are shared equitably, this project includes three evidence-based education and outreach programs designed to broaden participation in STEM fields.The research goal of this project is to test the hypotheses that humans can learn dynamic models of complex contact-rich machine dynamics, that they can and invert those models to control machine interactions with the environment, and that machines can exploit these facts to adapt their behavior to assist the human to perform desirable tasks. This goal will be pursued through three objectives. The first is to mathematically derive and computationally approximate inverse models for contact-rich dynamics. To achieve this objective, the PI will derive conditions that ensure that a forward model involving contact dynamics is invertible (exactly or approximately) and create algorithms that compute representations for the (approximate) inverse model. The second objective is to experimentally test whether humans control contact-rich dynamics as if they learned and inverted forward models, and to discern how sensorimotor pathways are integrated to implement the human’s controller. To do so, the project team will conduct human subject experiments using a novel teleoperation testbed to study trajectory tracking tasks involving intermittent contact events. The third objective will mathematically derive and experimentally test performance of human/machine co-adaptation algorithms based on game theory. The education goal of this project is to broaden participation in STEM fields by engaging high school and college students from underrepresented groups in hands-on research and design experiences focused on human/machine interaction.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.

这个学院早期职业发展（Career）基金旨在发现基本的工程原理，使人类和机器能够协同学习和控制当人类或机器间歇性地接触物体或环境中不平坦的地形时产生的复杂动态。尽管人类在驾驶飞机、驾驶汽车或进行机器人辅助手术时成功地学会了控制复杂的机器动力学，但这些应用避免了脚着地或手最初抓住物体时遇到的动力学突变。尽管在日常生活和工作中普遍存在丰富的接触动态交互，但最先进的机器人仍在努力建立和打破接触，无论是否有人类在循环中。在当前和未来的许多应用中-远程操作机器人，主动假肢和外骨骼，以及脑/机器接口，仅举几例-人类和机器将协同学习控制腿和手臂，因为它们与环境建立或断开接触。该项目将推进美国国家科学基金会的使命，促进科学的进步，促进国家健康、繁荣和福利，通过推进人类如何学习控制接触丰富的动力学，以及机器如何适应以确保安全和提高人/机器系统的性能。从长远来看，该项目的成果将使人类/机器人团队能够执行涉及与世界动态交互的复杂任务。由于许多工作正变得越来越自动化，而且由于许多人在生活中的某些时候会经历运动障碍，因此这项研究的目标对许多人的工作和/或健康有切实的好处。为了确保公平分享这些好处，该项目包括三个以证据为基础的教育和推广项目，旨在扩大STEM领域的参与。这个项目的研究目标是测试以下假设：人类可以学习复杂接触丰富的机器动力学的动态模型，他们可以和反转这些模型来控制机器与环境的交互，机器可以利用这些事实来调整它们的行为，以帮助人类执行理想的任务。这一目标将通过三个目标来实现。首先是数学推导和计算近似的逆模型的接触丰富的动力学。为了实现这一目标，PI将推导出确保涉及接触动力学的正演模型可逆（完全或近似）的条件，并创建计算（近似）逆模型表示的算法。第二个目标是通过实验测试人类是否像学习和倒转前向模型一样控制富含接触的动力学，并辨别如何整合感觉运动路径来实现人类的控制器。为此，项目团队将使用一种新型远程操作试验台进行人体实验，研究涉及间歇性接触事件的轨迹跟踪任务。第三个目标将数学推导和实验测试基于博弈论的人机协同适应算法的性能。该项目的教育目标是通过让来自代表性不足群体的高中生和大学生参与专注于人机交互的实践研究和设计经验，扩大STEM领域的参与。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。