Convergence Accelerator Phase I (RAISE): Safe Skill-Aligned On-The-Job Training with Autonomous Systems

NSF融合加速器支持以团队为基础的多学科努力，解决国家重要性的挑战，并在不久的将来显示出可交付成果的潜力。“融合加速器”第一阶段项目的更广泛影响/潜在效益源于增强我们未来劳动力的全球竞争力。尽管保持竞争力需要美国制造业的生产率随着人工智能（AI）和自主机器人的使用而提高，但先进的机器人系统目前要求劳动力至少拥有工程、计算机科学或人工智能的4年学位。目前的机器人技术本身并不支持适应性、安全性和可解释性，这一事实放大了这个问题。该项目将通过创建使用安全、自我解释、自适应机器人的自主在职培训平台来解决这些问题。为了实现这些目标，我们的团队采用了一种融合的方法，从智能辅导系统（ITS）、人工智能、机器人、制造过程、人类系统工程和认知科学的研究中汲取思想和工具。该项目的团队还包括解决将先进技术引入现有社会基础设施的法律和社会伦理挑战的专家。该项目与多个工业合作伙伴协调，综合了这些不同的方法，以实现一个自主在职培训平台，使我们的国家劳动力能够使用自主系统。“融合加速器”第一阶段项目旨在启动智能培训系统和自解释自主系统的发明、开发和评估，为使用自主系统的工作提供安全的在职培训。尽管自主系统在提高生产力方面具有巨大的潜力，但就目前的技术水平而言，这种潜力还无法实现。当前的培训范例是为以固定的功能和行为为特征的操作系统而设计的，针对今天的自治系统的工作培训提出了独特的挑战，而这些挑战并没有得到当前培训范例的解决。相比之下，根据定义，人工智能系统的功能和行为将每天都在变化。这个跨学科的项目将利用安全和可任务的自我解释自主系统来开发一类新的智能辅导系统，为使用自主系统的工作提供在职培训。在此过程中，它还将推进创建自我解释自主系统的方法，用于安全且符合工作场所法规的特定任务机器人行为的自动合成，以及用于评估协作的人类自主团队。此外，它将通过与人机协作先进制造领域的行业专家协商，开发可重复的试验台，推进这些领域的研究。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。

The NSF Convergence Accelerator supports team-based, multidisciplinary efforts that address challenges of national importance and show potential for deliverables in the near future. The broader impact/potential benefit of this Convergence Accelerator Phase I project stems from empowering the global competitiveness of our future workforce. Although staying competitive requires the productivity of US manufacturing to increase with the utilization of artificial intelligence (AI) and autonomous robotics, advanced robotic systems currently require a workforce with, at the least, 4-year degrees in engineering, computer science or AI. The problem is amplified by the fact that current robotics technologies do not inherently support adaptability, safety and explainability. This project will address these issues by creating autonomous on-the-job training platforms that use safe, self-explaining, adaptive robots. In order to achieve these objectives, our team employs a convergent approach drawing upon ideas and tools from research on intelligent tutoring systems (ITS), AI, robotics, manufacturing processes, human systems engineering, and cognitive science. The project's team also includes experts on resolving legal and socio-ethical challenges of bringing advanced technology to existing social infrastructure. This project synthesizes these diverse approaches in coordination with multiple industrial partners to enable an autonomous on-the-job training platform that would empower our national workforce for working with autonomous systems. This Convergence Accelerator Phase I project aims to initiate the invention, development and evaluation of intelligent training systems and self-explaining autonomous systems for providing safe on-the-job training for work with autonomous systems. Although autonomous systems have immense potential for empowering a highly productive workforce, this potential cannot be realized with the current state of the art. Training for work with today's autonomous systems presents unique challenges not addressed by current training paradigms, which are designed for operational systems characterized by fixed functionality and behavior. In contrast, AI systems will, by definition, change from day to day in their functionality and behavior. This interdisciplinary project will utilize safe and taskable self-explaining autonomous systems to develop a new class of intelligent tutoring systems that provide on-the-job training for work with autonomous systems. In the process, it will also advance methods for creating self-explaining autonomous systems, for the automated synthesis of task-specific robot behavior that is safe and compliant with workplace regulations, and for the evaluation of collaborative human-autonomy teamwork. In addition, it will advance research in these areas through the development of reproducible testbeds in consultation with industry experts in the domain of collaborative human-robot advanced manufacturing.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.