It is a matter of fact that even so called "primitive species" (like insects) readily outperform any human locomotive invention with respect to agility, adaptability and reliability - to name the least. The work at hand deals with two aspects that contribute to the pre-eminence of biological, terrestrial locomotor systems, namely motion control and muscle properties. In the first part of this work, a new, biologically well-founded approach for the control of articulated legs is presented. This controller, based on the detailed physiological knowledge of the stick insect's (Carausius morosus) leg control, redundantizes complex forward or backward kinematic calculations by dexterous employment of sensory feedback and muscle properties. This section shows that the collection of segmental coordination rules (which have been studied in the stick insect for several decades) is indeed able to generate periodic, robust middle leg stepping movements in a physical simulation of the animal. Furthermore, the controller is capable of handling stepping in the front and hind leg; although for hind leg stepping minor modifications were necessary. The second part of this work is about muscle modeling and it is divided into three chapters. Lynchpin of any motion is the muscle, and nowadays it is well-accepted that muscle properties are complex and highly variable. Hence, no trivial relationship between motor neuron activity and motion can be expected and typically, computer modeling is required to link the two. This part therefore first describes how a model of the stick insect's extensor tibiae muscle can be developed for individual muscles. The approach presented offers a way to measure and model all properties for the generation of a classical Hill-type model, in a single animal. Therefore it was necessary to reduce the number of measurements, stimulations and the overall time span of the experiment to a degree this muscle could take without severe loss in vitality. After this approach has been described, the next section deals with a possible application of individual muscle modeling. The variation of muscle model parameters is investigated for 10 different individuals. The question of parameter independence is addressed, and in fact it could be shown that there is co-variation between two different pairs of parameters. One correlation was found between two parameters modeling passive static force curve, the other between one parameter of the force-length and one of the force-activation curve. Both correlations suggest that the model can be reduced further. In the final section, isometric and isotonic simulations were performed with different model configurations. It is investigated how far averaging parameters of different animals would influence model performance. This is studied by comparing the error produced by four different model configurations, differing in their share of averaged parameters. Compared to a model entirely composed of averaged parameters, performance of the muscle specific model improves by approximately 40%
