IMOCO4.E targets to provide vertically distributed edge-to-cloud intelligence for machines, robots and other human-in-theloopcyber-physical systems having actively controlled moving elements. They face ever-growing requirements on long-termenergy efficiency, size, motion speed, precision, adaptability, self-diagnostic, secure connectivity or new human-cognitivefeatures.IMOCO4.E strives to perceive and understand complex machines and robots. The two main pillars of the project are digitaltwins and AI principles (machine/deep learning). These pillars build on the I-MECH reference framework and methodology,by adding new tools to layer 3 that delivers an intelligible view on the system, from the initial design throughout its entire lifecycle. For effective employment, completely new demands are created on the Edge layers (Layer 1) of the motion controlsystems (including variable speed drives and smart sensors) which cannot be routinely handled via available commercialproducts.Based on this, the subsequent mission is to bring adequate edge intelligence into the Instrumentation and Control Layers, toanalyse and process machine data at the appropriate levels of the feedback control loops and to synchronise the digitaltwins with either simulated or real-time physical world. At all levels, AI techniques are employable.Summing up, IMOCO4.E strives to deliver a reference platform consisting of AI and digital twin toolchains and a set ofmating building blocks for resilient manufacturing applications. The optimal energy efficient performance and easy(re)configurability, traceability and cyber-security are crucial.The IMOCO4.E reference platform benefits will be directly verified in applications for semicon, packaging, industrial roboticsand healthcare. Additionally, the project demonstrates the results in other generic “motion-control-centred” domains.The project outputs will affect the entire value chain of the production automation and application markets.