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

CHS：小型：安全关键场景中人机协作的形式化设计

• 批准号: 2007949
• 负责人: Hai Lin
• 金额: $ 49.98万
• 依托单位: University of Notre Dame
• 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 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2007949&HistoricalAwards=false
• 关键词: CHS; Small; Formal; Design; Human

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.

人机协作技术旨在将人类的优势与机器人相结合。机器人以更高的精度和速度和更长的耐力来处理重复的例程时出色。 另一方面，人类具有出色的感知能力，面对不确定性和意外情况，人类会更好。例如，人眼可以很容易地检测到透明玻璃上的划痕，但对于计算机视觉带来了极大的挑战。寻找帮助设计人与机器人（或人类和计算机）之间有效合作的原则是网络人类系统进步的核心。此外，许多网络人类系统的应用，例如联合组装制造，驾驶员援助和机器人辅助手术至关重要，并且需要通过保证的性能来实现复杂的高级任务。该项目旨在得出可证明的人类机器人协作设计理论，该理论可以保证完成高级复杂任务。 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从数据中学习模型的贝叶斯方法。通过学习的模型，研究了针对正式规格的人类机器人协作中的自动高级任务计划。当机器人与不同的个人互动或面对不确定的环境时，研究人员将进一步研究如何实现整体系统的在线（实时）改编。除了理论研究之外，团队还将通过真正的机器人测试床开发软件工具，并评估设计理论的有效性。该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响评估审查标准，认为值得通过评估来获得支持。