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Combining Optimization, Machine Learning, and Model Structure to Improve the Robustness and Agility of Modern Bipedal Machines

结合优化、机器学习和模型结构，提高现代双足机器的鲁棒性和敏捷性

基本信息

批准号：
1808051
负责人：
Jessy Grizzle
金额：
$ 40万
依托单位：
Regents of the University of Michigan - Ann Arbor
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2018
资助国家：
美国
起止时间：
2018-08-15 至 2021-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1808051&HistoricalAwards=false
关键词：
Combining Optimization Machine Learning Model

项目摘要

Bipedal robots are being built to aid in search and rescue, provide last mile delivery of packages, and to assist people in their homes. Lower-limb exoskeletons are being designed to help patients recover the ability to walk after strokes or even severe injuries resulting in paralysis. While the feedback control algorithms required to allow a bipedal robot to walk and a patient to safely operate a lower-limb exoskeleton are not identical, they share enough common elements that pursing their investigation in tandem is insightful and important. This project combines recent advances in the ability to quickly compute energy optimal solutions of bipedal dynamical systems with the mathematics of machine learning and geometric control theory to achieve unprecedented performance and safety in bipedal walking. The proposed research will greatly expand the class of robots for which feedback controllers can be designed with provable stability and it will significantly enhance the safety than can be achieved with exoskeletons that allow a paraplegic to walk without the use of crutches. One of the many technical challenges to be overcome in this research is the complexity of the mathematical models that describe the movement these legged machines. For example, printing out the symbolic model for the exoskeleton studied here would take thousands of pages. If a human ever opened the files to examine them, they would be incomprehensible. Yet, the PI and his students provide concrete means for designing feedback controllers for these machines and say deep things about how the closed-loop system will behave. This is the beauty of feedback control theory when it is married with modern computational tools. In addition, each year, the PI and his students share the excitement of engineering by giving tours of his robotics lab to hundreds of students, from grade school through high school, sharing the excitement and personal fulfillment of careers in STEM fields. Presidents of major universities and management teams of corporations visit his lab for the pure pleasure of seeing a robot doing something amazing and yet at the same time, almost ordinary: walking roughly like a human. The PI works with the media to share with the general public the excitement of cutting-edge engineering research and how it benefits society. The project seeks major advances in the theoretical conception and practical synthesis of feedback controllers for bipedal robots and lower-limb exoskeletons. The theory will be carefully tested on a Cassie-series bipedal robot and an exoskeleton. The theoretical thrust of the proposal aims to mitigate obstructions imposed by high-dimensional bipedal models (dimension 30 or more), without resorting to simplified pendulum models that are all too common in the robotics literature. The research seeks to work directly with the full model of the robot, making it possible to generate motions that exploit its full capabilities while respecting actuator limitations, ground contact forces, and terrain variability. The process begins with trajectory optimization to design an open-loop periodic walking motion of the high-dimensional model, and then adding to this solution, a carefully selected set of additional open-loop trajectories of the model that steer toward the nominal motion. Supervised Machine Learning is used to extract from the open-loop behavior (i.e., the collection of input and state trajectories) a low-dimensional state-variable realization (i.e., a low-dimensional manifold and associated vector field). The special structure of mechanical models of bipedal robots is used to embed the low-dimensional model in the original model in such a manner that it is both invariant and locally exponentially attractive, and show that this locally exponentially stabilizes the desired walking motion in the full state space of the robot. Transitions among periodic orbits will also be addressed.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.

人们正在建造两足机器人，以帮助搜索和救援，提供最后一英里的包裹递送，并帮助人们在家中。腿部外骨骼的设计是为了帮助患者在中风或甚至导致瘫痪的严重伤害后恢复行走能力。尽管允许两足机器人行走和患者安全操作下肢外骨骼所需的反馈控制算法并不完全相同，但它们有足够的共同点，因此同步推进他们的研究是有洞察力和重要的。该项目将快速计算两足动物动力系统能量最优解的能力的最新进展与机器学习的数学和几何控制理论相结合，以在两足行走中实现前所未有的性能和安全性。这项拟议的研究将极大地扩展反馈控制器可以设计为具有可证明稳定性的机器人的类别，并且它将比外骨骼实现的安全性显著提高，外骨骼允许截瘫患者不使用拐杖行走。在这项研究中需要克服的众多技术挑战之一是描述这些腿部机器运动的数学模型的复杂性。例如，打印出这里研究的外骨骼的符号模型需要数千页。如果有人打开这些文件来查看它们，它们将是不可理解的。然而，PI和他的学生提供了为这些机器设计反馈控制器的具体方法，并对闭环系统的行为进行了深入的讨论。这就是反馈控制理论与现代计算工具相结合时的美妙之处。此外，PI和他的学生每年都会通过带领数百名学生参观他的机器人实验室来分享工程方面的兴奋，从小学到高中，分享STEM领域职业生涯的兴奋和个人成就感。主要大学的校长和企业的管理团队访问他的实验室，纯粹是为了看到一个机器人在做一些令人惊叹的事情，但同时又几乎是普通的：像人类一样粗暴地行走。PI与媒体合作，与普通公众分享尖端工程研究的兴奋及其如何造福社会。该项目寻求在两足机器人和双腿外骨骼反馈控制器的理论构思和实用综合方面取得重大进展。这一理论将在卡西系列两足机器人和外骨骼上进行仔细的测试。该提案的理论主旨旨在减少高维两足动物模型(维度30或更多)造成的障碍，而不是求助于机器人文献中太常见的简化摆模型。这项研究寻求直接与机器人的完整模型一起工作，使其能够在考虑执行器限制、地面接触力和地形可变性的情况下产生充分利用其能力的运动。该过程以轨迹优化开始，以设计高维模型的开环周期性行走运动，然后向该解决方案添加一组精心选择的、朝向标称运动的模型的附加开环轨迹。有监督机器学习用于从开环行为(即，输入和状态轨迹的集合)中提取低维状态变量实现(即，低维流形和相关向量场)。利用双足机器人机械模型的特殊结构，将低维模型嵌入到原始模型中，使其既具有不变性，又具有局部指数吸引性，并证明在机器人的全状态空间中局部指数稳定期望的步行运动。这项裁决反映了NSF的法定使命，并通过使用基金会的智力优势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（12）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Toward Safety-Aware Informative Motion Planning for Legged Robots

为腿式机器人提供安全意识的信息性运动规划

DOI：
发表时间：
2021
期刊：
ArXivorg
影响因子：
0
作者：
Teng, Sangli;Gong, Yukai;Grizzle, Jessy;Ghaffari, Maani
通讯作者：
Ghaffari, Maani

Feedback Control Design for Robust Comfortable Sit-to-Stand Motions of 3D Lower-Limb Exoskeletons

DOI：
10.1109/access.2020.3046446
发表时间：
2021
期刊：
IEEE Access
影响因子：
3.9
作者：
M. E. Mungai;J. Grizzle
通讯作者：
M. E. Mungai;J. Grizzle

IEEE Access Special Section Editorial: Real-Time Machine Learning Applications in Mobile Robotics

IEEE Access 专题社论：移动机器人中的实时机器学习应用

DOI：
10.1109/access.2021.3090135
发表时间：
2021
期刊：
IEEE Access
影响因子：
3.9
作者：
Ucar, Aysegul;Grizzle, Jessy W.;Ghaffari, Maani;Wahde, Mattias;Akin, H. Levent;Baltes, Jacky;Bozma, H. Isil;Miro, Jaime Valls
通讯作者：
Miro, Jaime Valls

Combining trajectory optimization, supervised machine learning, and model structure for mitigating the curse of dimensionality in the control of bipedal robots