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Kurzfassung auf Englisch:

The walking movement of humans is graceful and robust. This movement is the result of synchronization between the neural mechanisms which generate rhythmic motion, and the dynamics of the skeletal structure. The overall movement is optimized by high-level control centers. Drawing inspiration from this mechanism, a hierarchical controller for bipedal locomotion of robots is proposed in this thesis. Artificial central pattern generators (CPGs) mimic the behavior of the neural mechanisms which produce rhythmic motion in animals. Many existing methods use CPG networks for bipedal locomotion, but most of them focus solely on the CPGs. The proposed controller augments the functionality of a CPG network by adding a novel high-level controller on top of it. Thus, at the lower level, a CPG network with feedback pathways controls the individual joints. The parameters of the CPG network are optimized by a genetic algorithm. At the higher level, a neural network modulates the output of the CPG network in order to optimize the robot's movements with respect to an overall objective. In this case, the objective is to minimize the lateral deviation while walking. The neural network is trained using reinforcement learning. The proposed controller was successfully used to produce stable bipedal locomotion for the NICO robot in simulation. Results of experiments show that the high-level controller was able to improve the performance of the low-level CPG network. Additionally, by comparing the performance of CPG networks with and without feedback mechanisms, the relative effectiveness of low-level feedback has been shown. The proposed controller is not strongly coupled to a particular robot model and is modular in design. The results obtained, by using this controller in simulation, encourage its use on the physical robot in the future.