NRI: FND: Improving Human-Robot Collaboration on Assembly Tasks by Anticipating Human Actions

实现对人类安全的机器人将为在复杂的装配任务中部署人类-机器人团队提供机会。人类可以执行需要灵活性的复杂任务，而机器人可以执行不需要高度灵活性的辅助任务。让人类和机器人近距离操作，同时利用它们的互补优势，可以显着提高人类的生产力，减少人类的工作压力。当人类在团队中工作时，重要的是团队成员要发展流畅的团队行为，人类-机器人团队也应该如此。这需要机器人助手适应人类队友的偏好，预测他们的行动并支持他们执行任务。该奖项支持基础研究，以适应多个机器人与人类队友在装配制造任务中合作的行动。 研究结果将有助于在装配制造任务中引入多个工业机器人，并将为美国制造业带来增长机会。 研究与研究生和本科生课程的整合将增强机器人和制造课程，丰富参与学生的学习经验。外展活动将教育和告知K-12学生关于机器人和制造业的职业机会。在人机混合细胞中有效适应多个机器人需要在人类偏好建模，人类行为预测和人类感知任务和运动规划方面取得根本性进展。这项研究将调查机器学习算法的设计，确定人类操作员在规范的装配任务的主导偏好的计算基础。算法将被开发用于生成任务计划，指定需要执行的任务，并将其分配给系统中的各种代理，并使用任务计划提示和工作单元监控来预测下一个人类行动。这项研究将探索和表征使用预测的行动，以适应多个机器人与人类队友的装配任务的行动的方法。这个奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

The realization of human-safe robots would present opportunities for the deployment of human-robot teams in complex assembly tasks. Humans can perform complex tasks that require dexterity, while robots can perform supporting tasks that do not require a high degree of dexterity. Having humans and robots operate in close proximity, while utilizing their complementary strengths, can significantly enhance human productivity and reduce job stress for humans. When humans work in teams, it is important for the team members to develop fluent team behavior and the same should hold for a human-robot team. This requires robotic assistants to adapt to the preferences of human teammates, anticipate their actions and support them in performing the task. This award supports fundamental research to enable adaptation of the actions of multiple robots collaborating with a human teammate in an assembly manufacturing task. Results from the research will facilitate introduction of multiple industrial robots in assembly manufacturing tasks and will result in growth opportunities for the US manufacturing industry. The integration of the research with graduate and undergraduate courses will enhance robotics and manufacturing curricula and enrich the learning experiences of the participating students. Outreach activities will educate and inform K-12 students about career opportunities in robotics and manufacturing.Effective adaptation of multiple robots in human-robot hybrid cells requires fundamental advances in human preference modeling, human action prediction and human-aware task and motion planning. This research will investigate computational foundations for the design of machine learning algorithms that identify the dominant preferences of human operators in canonical assembly tasks. Algorithms will be developed for generating task plans that specify tasks that need to be performed and assigning them to various agents in the system and for predicting the next human action using task plan cues and work cell monitoring. This research will explore and characterize methods for using the predicted actions to adapt the actions of multiple robots interacting with a human teammate on an assembly task.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.