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Ideally, artificial intelligent control systems incorporate an incremental learning capability, without having the need to be retrained offline. Moreover, some hardware\u2010based neural networks (HNNs) rely on complementary software simulators during training. Such approaches decrease their potential to implement real\u2010time adaption, in a similar manner to their biologic counterparts. Herein, an independent, bioinspired robotic vision system with an incremental learning ability is presented. The system is trained and operated by associating instructions with hieroglyphic symbols and used to maneuver a robotic vehicle. A neural processor, capable of incremental online learning, forms the basis for the decision\u2010making flow. 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