Interoperability and machinetomachine translation model with mappings to machine learning tasks Modern largescale automation systems integrate thousands to hundreds of thousands of physical sensors and actuators. Demands for more flexible reconfiguration of production systems and optimization across different information models, standards and legacy systems challenge current system interoperability concepts. Automatic semantic translation across information models and standards is an increasingly important problem that needs to be addressed to fulfill these demands in a costefficient manner under constraints of human capacity and resources in relation to timing requirements and system complexity. Here we define a translatorbased operational interoperability model for interacting cyberphysical systems in mathematical terms, which includes system identification and ontologybased translation as special cases. We present alternative mathematical definitions of the translator learning task and mappings to similar machine learning tasks and solutions based on recent developments in machine learning. Possibilities to learn translators between artefacts without a common physical context, for example in simulations of digital twins and across layers of the automation pyramid are briefly discussed.

Fast and Safe Policy Adaptation via Alignmentbased Transfer Applying deep reinforcement learning to physical systems, as opposed to learning in simulation, presents additional challenges in terms of sample efficiency and safety. Collecting large amounts of hardware demonstration data is timeconsuming and the exploratory behavior of reinforcement learning algorithms may lead the system into dangerous states, especially during the early stages of training. To address these challenges, we apply transfer learning to reuse a previously learned policy instead of learning from scratch. In this paper, we propose a method where given a source policy, policy adaptation is performed via transfer learning to produce a target policy suitable for realworld deployment. For policy adaptation, alignmentbased transfer learning is applied to trajectories generated by the source policy and their corresponding safe target trajectories. We apply this method to manipulators and show that the proposed method is applicable to both intertask and interrobot transfer whilst considering safety. We also show that the resulting target policy is robust and can be further improved with reinforcement learning.