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Robots Teaching Robots: Real-time Optimal Control of Complex Engineering Systems

机器人教学机器人：复杂工程系统的实时优化控制

基本信息

批准号：
2029181
负责人：
David Braun
金额：
$ 47.99万
依托单位：
Vanderbilt University
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：
2020-10-01 至 2024-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2029181&HistoricalAwards=false
关键词：
Robots Teaching Real time Optimal

项目摘要

This project introduces learner-helper robot pairs to enable the learner robot to use physical experimentation to improve its performance on repetitive task, without accurate analytical or numerical models. The specific challenge is that these tasks -- for example walking on two legs or riding a bicycle -- require a minimal necessary level of performance, below which the robot is unable to function. In the examples, this minimal level of ability corresponds to not falling over. The helper satisfies these minimal requirements while the learner uses repeated trials to improve its performance. For example, the helper might suspend the two-legged walker from a traveling harness or move alongside the bicycle robot providing an additional point of support. As the learner-helper team masters the task, the amount of assistance that the helper can apply is gradually reduced, until the learner is performing at a high level on its own. An analogy is a child learning to ride a bike with the help of an adult moving alongside. The new control technique will enable robots to teach robots in training lines of future factories similar to robots currently used in assembly lines of manufacturing companies. Therefore, the results of this research will benefit the U.S. economy and society. This research also involves several disciplines including mechanical, electrical, computer, and control engineering. The multi-disciplinary approach is expected to broaden the participation of underrepresented groups in research and positively impact engineering education.Optimal control is a branch of control theory that has the potential to revolutionize the creation of intelligent engineering systems, industrial robots, surgical robots, and assistive robots that can improve by repeated experience, somewhat similar to humans. There are many optimal control techniques to control engineering systems. However, almost all currently available techniques require high-fidelity models or a large amount of measured data to mitigate the so-called simulation-reality gap; the gap between the optimal performance predicted by computer simulations and the non-optimal performance observed in real engineering applications. This award supports fundamental research to close the simulation-reality gap when optimal control is applied to engineering systems. Model-based optimal control techniques enable efficient computation but they are subject to conservative control performance. Data-driven optimal control techniques mitigate the detrimental effect of uncertain models, but to do so, they require a large amount of training data. Therefore, scientific barriers must be overcome to realize the full application potential of optimal control techniques. This research will address the knowledge gap that limits the potential and theoretical promise of optimal control theory when applied to complex engineering systems. The new technique promotes optimization of system performance via real-time experiments guided by dedicated teacher robots, instead of optimizing system performance guided only by uncertain model-based predictions and measured data. The technique delivers a transformative approach to control the class of complex, underactuated, and unstable robots, for which obtaining high-fidelity models is challenging, while gathering training data is time-consuming. The research outcomes could potentially provide mainstream paradigms in creating next-generation intelligent machines.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.

该项目引入了学习者-助手机器人对，使学习者机器人能够使用物理实验来提高其在重复任务上的表现，而无需精确的分析或数值模型。具体的挑战是这些任务——例如两条腿行走或骑自行车——需要最低限度的必要性能水平，低于该水平机器人就无法发挥作用。在示例中，该最低能力水平对应于不摔倒。帮助器满足这些最低要求，而学习器则通过重复试验来提高其性能。例如，助手可以将两足步行器悬挂在行进安全带上，或者沿着自行车机器人移动，提供额外的支撑点。随着学习者-帮助者团队掌握任务，帮助者可以应用的帮助量逐渐减少，直到学习者自己表现出高水平。打个比方，孩子在成人的帮助下学习骑自行车。新的控制技术将使机器人能够在未来工厂的训练线上教导机器人，类似于目前制造公司装配线上使用的机器人。因此，这项研究成果将有利于美国经济和社会。这项研究还涉及机械、电气、计算机和控制工程等多个学科。多学科方法有望扩大代表性不足群体对研究的参与，并对工程教育产生积极影响。最优控制是控制理论的一个分支，有可能彻底改变智能工程系统、工业机器人、手术机器人和辅助机器人的创建，这些机器人可以通过重复经验进行改进，有点类似于人类。有许多优化控制技术来控制工程系统。然而，目前几乎所有可用的技术都需要高保真模型或大量测量数据来缩小所谓的模拟与现实差距；计算机模拟预测的最佳性能与实际工程应用中观察到的非最佳性能之间的差距。该奖项支持基础研究，以缩小将最优控制应用于工程系统时的模拟与现实差距。基于模型的最优控制技术可以实现高效计算，但它们受到保守控制性能的影响。数据驱动的最优控制技术减轻了不确定模型的不利影响，但要做到这一点，它们需要大量的训练数据。因此，必须克服科学障碍才能充分发挥最优控制技术的应用潜力。这项研究将解决限制最优控制理论应用于复杂工程系统时的潜力和理论前景的知识差距。新技术通过专用教师机器人引导的实时实验来促进系统性能的优化，而不是仅通过基于模型的不确定预测和测量数据来优化系统性能。该技术提供了一种变革性的方法来控制复杂、欠驱动和不稳定的机器人，对于这些机器人来说，获得高保真模型具有挑战性，而收集训练数据又非常耗时。研究成果有可能为创建下一代智能机器提供主流范式。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。