Human-Robot Collaboration for Flexible Manufacturing

作为高复杂性、小批量产品的制造商，制造标准有很大的差异，我们在制造过程中广泛采用机器人技术的一个障碍是所需的编程数量--目前很难看到足够的投资回报。机器人能够理解在与人合作期间通过模仿学习自主产生解决方案所需的内容，这将大大减少编程时间，从而实现敏捷、可重新配置的制造。目前机器人学和人工智能对人与Cobot(协作机器人)之间的交互和协作的研究已经导致了意图识别的机器学习模型的设计，例如用于预测协作任务的目标(例如Vinanzi等人)。2019年)以及通过动觉模仿和语言教学进行动作学习(钟等人)。2019年)。这些模型是在实验室环境中为简化的联合任务情景开发的。人工智能领域的最新进展(例如，深度学习；Sunderhauf等人)。2018)提供了一个及时扩展意向阅读模型的机会，以便在现实的行业环境中进行协作。这将导致系统了解其环境和需要与其交互的组件，使用视觉和动作识别AI方法以及CAD模型/数字双胞胎的询问。博士研究任务：1.用于意图阅读和模仿学习的机器学习的文献范围以及人-机器人联合任务和AI方法的选择2.用于人-机器人合作和学习任务的训练数据集生成3.基于数据集的机器学习模拟和人-机器人交互实验的评估该项目直接与EPSRC在机器人学和人工智能领域的优先领域保持一致。

As manufacturers of high complexity, low volume products with considerable variation in build standards, one obstacle to our adoption of widespread robotics in the manufacturing process is the amount of programming required - it is currently difficult to see sufficient return on investment. The ability for the robot to understand what is required during collaboration with people to produce a solution autonomously and via imitation learning, would drastically reduce programming times, resulting in agile, reconfigurable manufacturing. Current robotics and AI research on human-robot interaction and collaboration between people and cobots (collaborative robots) has led to the design of machine learning models of intention recognition, e.g. for the prediction of the goal of a cooperative task (e.g. Vinanzi et al. 2019) and for action learning via kinaesthetic imitation and linguistic instruction (Zhong et al. 2019). These models have been developed for simplified joint task scenarios, in laboratory settings. The latest advances in AI (e.g. deep learning; Sunderhauf et al. 2018) offer a timely opportunity to scale up intentional reading models for collaboration in realistic, industry setting scenarios. This will lead to the system understanding of its environment and the components with which it is required to interact, using a combination of visual and action recognition AI methods and interrogation of CAD models/digital twin.PhD research tasks:1. Scope of the literature on machine learning for intention reading and imitation learning and selection of human-robot joint task and of AI methods 2. Training dataset generation for the human-robot cooperation and learning task3. Machine learning simulations on dataset and a valuation in human-robot interaction experimentsThe project is directly aligned with EPSRC's priority areas in Robotics and AI.