Industry 4.0 derived technologies have the potential to enable a new wave of digital manufacturing solutions for semi and fully automated production. In addition, this paradigm enco m p a s ses the use of communication technologies to transmit data to processing stations as well as the utilization of cloud based computational resources for data mining. Despite the rise in automation, future manufactu rin g systems will initially still require humans in the loop to provide supervisory level mediation for even the most autonomous production scenarios. Through a structured review, this paper details a number of key technologies that are most likely to shape this future and describes a range of scenarios for their use in delivering human mediated automated and autonomous production. This paper argues that in all cases of future manufacturing management it is key that the human has oversight of critical information flows and remains an active participant in the delivery of the next generation of production systems.

In this paper, a grounding framework is proposed that combines unsupervised and supervised grounding by extending an unsupervised grounding model with a mechanism to learn from explicit human teaching. To investigate whether explicit teaching improves the sample efficiency of the original model, both models are evaluated through an interaction experiment between a human tutor and a robot in which synonymous shape, color, and action words are grounded through geometric object characteristics, color histograms, and kinematic joint features. The results show that explicit teaching improves the sample efficiency of the unsupervised baseline model.

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