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In the mid‐1980s, a major paradigm shift took place toward embodiment, and computer science started to become more practical than theoretical algorithms and approaches. Embedded systems started to appear in all kinds of forms to aid humans in everyday life. Controllers for trains, airplanes, elevators, air conditioners, and software for translation and audio manipulation are some of the most important ones, to name a few [23].

Embodied artificial intelligence is a broad term, and those successes were for sure great ones to start with. Yet, it could clearly be seen that it was a huge room for improvement. Theoretically, the ultimate goal of AI is to not only master any given algorithm or task that is given to but also gain the ability to multitask and get to human‐level intelligence, and that as mentioned requires meaningful interaction with the real world. There are many specialized robots for a vast set of tasks out there, especially in large industries, which can do the assigned task to perfection, let it be cutting different metals, painting, soldering circuits, and many more. However, until one single machine emerges to have the ability to do different tasks or at least a small subset of them by itself and not just by following orders, it cannot be called intelligence.