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.

该项目介绍了学习者助手机器人对，以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性能，而无需准确的分析或数值模型。具体的挑战是，这些任务（例如在两条腿上行走或骑自行车）需要最低的必要水平，在此下面机器人无法运行。在示例中，这种最小的能力水平对应于没有掉下来。当学习者使用重复试验来提高其性能时，助手满足了这些最低要求。例如，助手可能会暂停两足的步行者从行进的安全带上悬挂，或者在自行车机器人旁边移动，从而提供额外的支撑点。随着学习者助手团队掌握任务，助手可以申请的帮助量逐渐减少，直到学习者独自一人表现出色为止。类比是一个孩子在成年人陪伴下学习骑自行车的孩子。新的控制技术将使机器人能够在未来工厂的培训行中教机器人，类似于当前在制造公司组装产品线中使用的机器人。因此，这项研究的结果将使美国经济和社会受益。这项研究还涉及几个学科，包括机械，电气，计算机和控制工程。预计多学科方法将扩大代表性不足的群体参与研究和积极影响工程教育。最佳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，外科机器人和辅助机器人的创建，并且可以通过反复的经验来改善与人类类似的经验，从而改善与人类相似的经验。控制工程系统有许多最佳控制技术。但是，几乎所有当前可用的技术都需要高保真模型或大量测量数据来减轻所谓的模拟现实差距。计算机模拟预测的最佳性能与在实际工程应用中观察到的非最佳性能之间的差距。当将最佳控制应用于工程系统时，该奖项支持基础研究，以缩小模拟现实差距。基于模型的最佳控制技术可实现有效的计算，但要受到保守的控制性能。数据驱动的最佳控制技术减轻了不确定模型的有害效果，但为此，它们需要大量的培训数据。因此，必须克服科学障碍，以实现最佳控制技术的全面应用潜力。这项研究将解决知识差距，该差距限制了应用于复杂工程系统的最佳控制理论的潜力和理论希望。新技术通过由专用教师机器人引导的实时实验促进了系统性能的优化，而不是仅通过不确定的基于模型的预测和测量数据来优化系统性能。该技术提供了一种转型方法来控制复杂，不足和不稳定的机器人的类别，为此，获得高保真模型的机器人是具有挑战性的，同时收集培训数据很耗时。研究成果有可能为创建下一代智能机器的主流范式提供主流范式。该奖项反映了NSF的法定任务，并且使用基金会的知识分子优点和更广泛的影响审查标准，被认为值得通过评估来获得支持。