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

人-机器人协作技术旨在将人类的优势与机器人的优势结合起来。机器人擅长处理重复的例行公事，具有更高的精度和速度，以及更长的耐力。另一方面，人类具有更强的感知能力，在面对不确定和意外情况时要好得多。例如，透明玻璃上的划痕很容易被人眼检测到，但对计算机视觉来说是一个极其困难的挑战。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，如关节装配制造、驾驶员辅助和机器人辅助手术，都是安全关键的，需要在保证性能的情况下完成复杂的高级任务。本项目旨在得出一种可证明正确的人-机器人协同设计理论，以确保高级别复杂任务的完成。该项目的研究可以在许多涉及人-机器人和人机协作的真实世界应用中增加安全性和可信度，例如服务机器人、自动化制造系统、应急响应和未知空间的探索。该项目采用一种新的模型，称为向量自回归部分可观测马尔可夫决策过程(VAR-POMDP)来管理不确定性，并使用非参数贝叶斯方法从数据中学习该模型。在此基础上，研究了人机协作中基于形式化规范的高层任务自动规划问题。研究团队将进一步研究，当机器人与不同的个体互动或面对不确定的环境时，如何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，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.