Abstract For many contemporary manufacturing processes, autonomous robotic operators have become ubiquitous. Despite this, the number of human operators within these processes remains high, and as a consequence, the number of interactions between humans and robots has increased in this context. This is a problem, as human beings introduce a source of disturbance and unpredictability into these processes in the form of performance variation. Despite the natural human aptitude for flexibility, their presence remains a source of disturbance within the system and make modelling and optimization of these systems considerably more challenging, and in many cases impossible. Improving the ability of robotic operators to adapt their behaviour to variations in human task performance is, therefore, a significant challenge to be overcome to enable many ideas in the larger intelligent manufacturing paradigm to be realised. This work presents the development of a methodology to effectively model these systems and a reinforcement learning agent capable of autonomous decision-making. This decision-making provides the robotic operators with greater adaptability, by enabling its behaviour to change based on observed information, both of its environment and human colleagues. The work extends theoretical knowledge on how learning methods can be implemented for robotic control, and how the capabilities that they enable may be leveraged to improve the interaction between robots and their human counterparts. The work further presents a novel methodology for the implementation of a reinforcement learning-based intelligent agent which enables a change in behavioural policy in robotic operators in response to performance variation in their human colleagues. The development and evaluation are supported by a generalized simulation model, which is parameterized to enable appropriate variation in human performance. The evaluation demonstrates that the reinforcement agent can effectively learn to make adjustments to its behaviour based on the knowledge extracted from observed information, and balance the task demands to optimise these adjustments.

中文翻译：

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促进制造业中人机交互的强化学习

摘要 对于许多当代制造过程，自主机器人操作员已变得无处不在。尽管如此，这些流程中的人工操作员数量仍然很高，因此，在这种情况下，人与机器人之间的交互数量有所增加。这是一个问题，因为人类以性能变化的形式在这些过程中引入了干扰源和不可预测性。尽管人类天生具有灵活性，但它们的存在仍然是系统内的干扰源，并使这些系统的建模和优化更具挑战性，并且在许多情况下是不可能的。因此，提高机器人操作员使其行为适应人类任务表现变化的能力是，要实现更大的智能制造范式中的许多想法，这是一个需要克服的重大挑战。这项工作提出了一种方法来有效地建模这些系统和能够自主决策的强化学习代理。这种决策为机器人操作员提供了更大的适应性，使其行为能够根据观察到的环境和人类同事的信息而改变。这项工作扩展了关于如何为机器人控制实施学习方法的理论知识，以及如何利用它们启用的功能来改善机器人与其人类对应物之间的交互。这项工作进一步提出了一种用于实施基于强化学习的智能代理的新方法，该方法可以改变机器人操作员的行为策略，以响应其人类同事的表现变化。开发和评估得到一个通用模拟模型的支持，该模型被参数化以实现人类表现的适当变化。评估表明，强化代理可以有效地学习根据从观察到的信息中提取的知识对其行为进行调整，并平衡任务需求以优化这些调整。它被参数化以实现人类表现的适当变化。评估表明，强化代理可以有效地学习根据从观察到的信息中提取的知识对其行为进行调整，并平衡任务需求以优化这些调整。它被参数化以实现人类表现的适当变化。评估表明，强化代理可以有效地学习根据从观察到的信息中提取的知识对其行为进行调整，并平衡任务需求以优化这些调整。