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

2019冠状病毒病大流行加速了许多行业对远程工作的采用。员工远程工作（通常是在家工作）的能力，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远程工作成为可能，但大多数制造业工人却无法做到，因为所需的基础设施尚不存在。这个未来制造（FM）项目将研究一种适应性强、可扩展的机器人远程操作系统，使工厂工人能够远程工作。这项研究将使制造业和劳动力受益，增加制造业就业机会，改善工作条件和安全。通过将人在环设计与机器学习相结合，这项研究可以将自动化在制造业中的应用扩大到新的任务中。除了制造业，这项研究还将降低机器人系统在现实世界中广泛应用的门槛，比如辅助机器人和服务机器人。研究团队正在与纽约设计公司和拉瓜迪亚社区学院合作，将研究成果转化为工业合作伙伴，并制定培训计划，以教育和准备未来的制造业劳动力。本研究提出了实现人在环装配的三个关键思想：首先，该系统使用物理场景理解算法，将真实世界的机器人工作空间转换为虚拟的可操作三维场景表示。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，在给定任务目标和机器人配置的情况下，系统使用目标驱动的强化学习算法来推断有效的规划策略。该系统可以克服现有远程操作系统的一些限制。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，该系统允许操作员在不具备机器人硬件和配置的专业知识的情况下指定任务目标。通过在底层控制中使用强化学习，系统可以更广泛地应用于新的任务和硬件。该奖项由民用机械和制造创新、电气、通信和网络系统、计算机和网络系统、本科教育、行为和认知科学和网络物理系统、NSF科学、技术、工程和数学奖学金以及先进技术教育计划共同资助。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 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.