Artificial intelligence is regarded as one of the most groundbreaking developments of recent times. However, in controlling autonomous systems we are still far from achieving the human motor intelligence of a newborn or young child. In this talk I will discuss why current algorithms for autonomous systems and robot learning methods have not yet reached the required autonomy and performance needed to enter daily life. I will present current developments of biologically inspired decision models for autonomous systems and will discuss probabilistic prediction models that can be implemented in massively parallelizable neural networks. These neural networks are trained by a combination of supervised and unsupervised neuroinspired learning rules and enable complex decisions based on learned internal prediction models. The efficient learning rules allow the model to react to new environmental conditions within seconds and to process high dimensional tactile and visual data. These model properties are essential for adaptive, reliable, explainable and robust artificial systems.