![]() ![]() 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. 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. This work presents the development of a methodology to effectively model these systems and a reinforcement learning agent capable of autonomous decision-making. 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. 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. ![]() This is a problem, as human beings introduce a source of disturbance and unpredictability into these processes in the form of performance variation. 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. For many contemporary manufacturing processes, autonomous robotic operators have become ubiquitous.
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June 2023
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