FMRG: Adaptable and Scalable Robot Teleoperation for Human-in-the-Loop Assembly

COVID-19疫情加速了许多行业采用远程工作。员工远程工作的能力，通常是在家里，已经成为一个组织的长期弹性和增长潜力的关键。然而，尽管软件和网络的进步使信息工作者能够远程工作，但大多数制造业工人却不能，因为所需的基础设施并不存在。这个未来制造（FM）项目将研究一个适应性强、可扩展的机器人远程操作系统，允许工厂工人远程工作。这项研究将通过增加制造业就业机会和改善工作条件和安全，使制造业和劳动力都受益。通过将人在环设计与机器学习相结合，这项研究可以将制造业自动化的应用扩展到新的任务。除了制造业，这项研究还将降低将机器人系统用于各种现实应用的准入门槛，例如辅助和服务机器人。该研究团队正在与NYDesigns和拉瓜迪亚Community College合作，将研究成果转化为工业合作伙伴，并开发培训计划，以教育和准备未来的制造业劳动力。这项研究提出了三个关键想法，以实现人在环装配：首先，该系统使用物理场景理解算法，将现实世界的机器人工作空间转换为虚拟可操纵的三维场景表示。接下来，将使用三维虚拟现实用户界面，允许用户使用此场景表示指定高级任务目标。最后，该系统使用目标驱动的强化学习算法来推断一个有效的规划策略，给定的任务目标和机器人配置。该系统可以克服现有遥操作系统的一些局限性。通过使用物理场景表示将高级任务规划与低级机器人控制分离，该系统允许操作员指定任务目标，而无需机器人硬件和配置的专业知识。通过将强化学习用于低级控制，系统更适用于新任务和硬件。该奖项由土木机械和制造创新，电气，通信和网络系统，计算机和网络系统，本科教育，行为和认知科学以及网络物理系统，NSF科学，技术，工程和数学奖学金，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。

The COVID-19 pandemic has accelerated the adoption of remote working in many industries. The ability for employees to work remotely, often from home, has become crucial to an organization's long-term resilience and growth potential. However, while advances in software and networking have made it possible for information workers to work remotely, most manufacturing workers cannot, because the infrastructure that is needed doesn't exist. This Future Manufacturing (FM) project will research an adaptable and scalable robot teleoperation system that allows factory workers to work remotely. The research will benefit both the manufacturing industry and the workforce by increasing access to manufacturing employment and improving working conditions and safety. By combining human-in-the-loop design with machine learning, this research can broaden the adoption of automation in manufacturing to new tasks. Beyond manufacturing, the research will also lower the entry barrier to using robotic systems for a wide range of real-world applications, such as assistive and service robots. The research team is collaborating with NYDesigns and LaGuardia Community College to translate research results to industrial partners and develop training programs to educate and prepare the future manufacturing workforce.This research suggests three key ideas to enable human-in-the-loop assembly: First, the system uses a physical scene understanding algorithm that converts the real-world robot workspace into a virtual manipulable three-dimensional scene representation. Next, a three-dimensional Virtual Reality user interface will be used to allow users to specify high-level task goals using this scene representation. Finally, the system uses a goal-driven reinforcement learning algorithm to infer an effective planning policy, given the task goals and the robot configuration. This system can overcome several limitations of existing teleoperation systems. By separating high-level task planning from low-level robot control using a physical scene representation, the system allows the operator to specify task goals without having expert knowledge of the robot hardware and configuration. By using reinforcement learning for low-level control, the system is more generalizable to new tasks and hardware.This award is co-funded by the Divisions of Civil Mechanical and Manufacturing Innovation, Electrical, Communications and Cyber Systems, Computer and Network Systems, Undergraduate Education, and Behavioral and Cognitive Sciences and the Cyber Physical Systems, NSF Scholarships in Science, Technology, Engineering, and Mathematics, and Advanced Technological Education Programs.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.