CHS: Small: Formal Design of Human Robot Collaboration in Safety Critical Scenarios

人机协作技术旨在将人类的优势与机器人的优势联合收割机结合起来。机器人擅长以更高的精度和速度以及更长的耐力处理重复的例程。 另一方面，人类具有上级感知能力，在面对不确定性和意外情况时表现得更好。例如，透明玻璃上的划痕可以很容易地被人眼检测到，但对计算机视觉来说是一个非常困难的挑战。寻找原则来帮助设计人类和机器人（或一般的人类和计算机）之间的有效协作是网络人类系统进步的核心。此外，许多网络人类系统应用，如关节组件制造、驾驶员辅助和机器人辅助手术，都是安全关键，需要在保证性能的情况下完成复杂的高级任务。本项目旨在推导出一种可证明正确的人机协作设计理论，以保证高层次复杂任务的完成。本项目的研究成果可以在服务机器人、自动化制造系统、应急响应和未知空间探索等涉及人机和人机协作的许多实际应用中提高安全性和可信度，从而造福社会。本项目采用了一种新的模型，称为向量自回归部分可观测马尔可夫决策过程（VAR-POMDP），以管理不确定性，它使用非参数贝叶斯方法从数据中学习模型。利用该模型，研究了人机协作中基于形式化规范的高级任务自动规划问题。研究人员将进一步研究如何在机器人与不同个体互动或面对不确定环境时实现整个系统的在线（实时）适应。除了理论研究，该团队还将开发软件工具，并通过真实的机器人试验台评估设计理论的有效性。该奖项反映了NSF的法定使命，并被认为值得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来支持。

Human-robot collaboration technologies aim to combine the strengths from humans with those of robots. Robots excel at handling repeated routines with much higher precision and speed, and longer endurance. Humans, on the other hand, have superior perception capabilities and are much better in face of uncertainties and unexpected situations. For example, a scratch on a transparent glass can be easily detected by human eyes but presents an extremely hard challenge for computer vision. Finding principles to help design effective collaboration between humans and robots (or humans and computers in general) is core to advances in cyber-human systems. In addition, many cyber-human system applications, such as joint assembly manufacturing, driver assistance, and robot-assisted surgery are safety critical and need to achieve complex high-level tasks with guaranteed performance. This project aims to derive a provably-correct human-robot collaboration design theory that can guarantee the accomplishment of high-level complex missions. Research from this project can benefit society by increasing the safety and trustworthiness in the many real-world applications involving human-robot and human-computer collaborations such as service robots, automated manufacturing systems, emergency responses, and exploration of unknown spaces.This project adopts a new model, called vector auto-regressive partially observable Markov decision process (VAR-POMDP), to manage uncertainties, and it uses non-parametric Bayesian methods to learn the model from data. With the learned model, an automatic high-level task planning in human-robot collaboration with respect to formal specifications is studied. The team of researchers will further study how to achieve online (real-time) adaptations of the overall system when robots are interacting with different individuals or facing uncertain environments. Beyond theoretical studies, the team will develop software tools and evaluate the effectiveness of the design theory through a real robotic test-bed.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.