In order to leave the factory floors and research labs, future robots must abandon their stiff and pre-programmed movements and be capable to learn complex policies. These control policies must be adaptive to the inevitable changes of the physical world and must only select feasible actions. Current black-box function approximation approaches are not sufficient for such tasks as these approaches overfit to the training domain and commonly perform damaging actions. In this talk, I want to introduce my research focusing on robot learning for physical robots by using inductive biases and deep learning. This approach combines the representational power of deep networks and directly includes the feasibility and robustness as inductive bias within the learning problem to enable the application to the physical system. Using this approach we showed that deep networks can be constrained to learn physically plausible models and optimal feedback controller by incorporating domain knowledge and demonstrated the applicability to the physical system.