CPS:Medium:Collaborative Research: Safe Learning in Co-robots--Theory, Experiments and Education

该奖项将支持可扩展和安全的协作网络物理系统的基础研究。具体来说，该项目将解决如何平衡大型人机团队的适应性和安全性的问题。虽然预编程的机器人可以在完全已知且不变的工作空间中表现良好，但在现实条件下完成复杂任务需要适应意外情况的能力。这种适应性可以使用人工智能或机器学习技术来提供，但由此产生的机器人行为变得不那么可预测，这反过来又使得难以保证安全的人机交互。该项目通过使用机器学习与控制理论和非线性动力学技术的创新集成来解决保证安全的挑战。与目前的方法相比，这里研究的方法将是可扩展的，也就是说，即使当交互的人类和机器人的数量变得很大时，它们也将保持实用。许多重要的应用程序可以从该项目的结果中受益，包括救灾，救援任务，国土安全和辅助医疗保健。该项目还将开发一个用于协作式人机工程学的教学平台，用于在大班中教授协作式机器人技术，并帮助扩大代表性不足的群体在研究中的参与。在共享物理工作空间中与人类合作的机器人被称为co-robots或cobots。这项研究将解决安全和性能保证的基本挑战，因为协作的人机网络物理系统从模型驱动的控制方法转向数据驱动的方法。特别是，该项目将专注于由人类和机器人群体执行的数据丰富的迭代任务，以及人类与机器人和机器人与机器人交互力建模复杂的动态挑战性任务。统计学习理论将与预测控制理论合并，在学习过程中使用基于物理和数据驱动的模型。在正在研究的合作机器人网络物理系统中，机器人和人类模型将使用数据馈送实时更新。在每个机器人中，预测控制器将使用这些模型来预测机器人运动和人类交互，并采取相应的安全和协作行动。该项目产生的新理论将在学习过程中为性能改进和安全提供严格的统计保证。该奖项反映了NSF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 award will support fundamental research on scalable and safe collaborative cyber physical systems. Specifically, this project will address the question of how to balance adaptability with safety for large human-robot teams. While pre-programmed robots can perform well in a perfectly known and unchanging workspace, accomplishing complex tasks under real-world conditions requires the ability to adapt to unexpected circumstances. This adaptability can be provided using artificial intelligence or machine learning techniques, but the resulting robot behavior becomes less predictable, which in turn makes it difficult to guarantee safe human-robot interactions. This project addresses the challenge of guaranteed safety by using an innovative integration of machine learning with techniques from control theory and nonlinear dynamics. In contrast to current approaches, the methods studied here will be scalable, that is, they will remain practical to implement even when the number of interacting humans and robots become large. Many important applications can benefit from the results of this project, including disaster relief, rescue missions, homeland security, and assisted healthcare. This project will also develop a teaching platform for collaborative human-robot engineering that will be used to teach collaborative robotics in large classes, and to help broaden the participation of underrepresented groups in research.Robots that collaborate with human partners in a shared physical workspace are called co-robots or cobots. This research will address the fundamental challenge of safety and performance guarantees as collaborative human-robot cyber physical systems move from model-driven control approaches to data-driven methods. In particular, the project will focus on data-rich iterative tasks performed by groups of humans and robots, and dynamically challenging tasks where human-robot and robot-robot interaction forces are complex to model. Statistical learning theory will be merged with predictive control theory using a mix of physics-based and data-driven models in the learning process. In the co-robot cyber physical systems under study, robot and human models will be updated in real-time using data feeds. Within each robot such models will be used by a predictive controller to forecast robot motion and human interaction, and to take corresponding safe and collaborative actions. The new theory resulting from this project will provide statistically rigorous guarantees of performance improvement and safety during the learning process.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.