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

本项目引入学习-辅助机器人对，使学习机器人在没有精确的分析或数值模型的情况下，通过物理实验来提高其在重复性任务中的表现。具体的挑战是，这些任务——例如用两条腿走路或骑自行车——需要一个最低的必要性能水平，低于这个水平，机器人就无法工作。在这些例子中，这种最低水平的能力对应于不摔倒。辅助者满足这些最低要求，而学习者通过反复试验来提高其表现。例如，助手可以将两条腿的步行器挂在旅行背带上，或者与自行车机器人一起移动，提供额外的支撑点。随着学习者-帮助者团队掌握了任务，帮助者可以提供的帮助量逐渐减少，直到学习者自己达到较高的水平。打个比方，一个孩子在旁边移动的成年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培训线上教授机器人，类似于目前在制造公司的装配线上使用的机器人。因此，这项研究的结果将有利于美国的经济和社会。该研究还涉及机械、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的群体在研究中的参与，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，它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造，这些机器人可以通过重复的经验来改进，有点类似于人类。有许多最优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缓解所谓的模拟与现实差距；计算机模拟预测的最优性能与实际工程应用中观察到的非最优性能之间的差距。该奖项支持在最优控制应用于工程系统时缩小模拟与现实差距的基础研究。基于模型的最优控制技术能够提高计算效率，但控制性能保守。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，要充分发挥最优控制技术的应用潜力，必须克服科学上的障碍。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。新技术通过由专门的教师机器人指导的实时实验来促进系统性能的优化，而不是仅仅通过基于不确定模型的预测和测量数据来优化系统性能。该技术提供了一种革命性的方法来控制一类复杂、欠驱动和不稳定的机器人，对于这些机器人，获得高保真模型是具有挑战性的，而收集训练数据则是耗时的。研究成果可能为创造下一代智能机器提供主流范例。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。

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.