How is load sensed by receptors, and how is this sensory information used to guide locomotion? Many insights in this domain have evolved from comparative studies since it has been realized that basic principles concerning load sensing and regulation can be found in a wide variety of animals, both vertebrate and invertebrate. Feedback about load is not only derived from specific load receptors but also from other types of receptors that previously were thought to have other functions. In the central nervous system of many species, a convergence is found between specific and nonspecific load receptors. Furthermore, feedback from load receptors onto central circuits involved in the generation of rhythmic locomotor output is commonly found. During the stance phase, afferent activity from various load detectors can activate the extensor part in such circuits, thereby providing reinforcing force feedback. At the same time, the flexion is suppressed. The functional role of this arrangement is that activity in antigravity muscles is promoted while the onset of the next flexion is delayed as long as the limb is loaded. This type of reinforcing force feedback is present during gait but absent in the immoble resting animal.

The construction of artificial walking machines has been a challenging task for engineers for several centuries. Advances in computer technology have stimulated this research in the past two decades, and enormous progress has been made, particularly in recent years. Nevertheless, in comparing the walk of a six-legged robot with the walk of an insect, the immense differences are immediately obvious. The walking of an animal is much more versatile, and seems to be more effective and elegant. Thus it is useful to consider the corresponding biological mechanisms in order to apply these or similar mechanisms to the control of walking legs in machines. Until recently, little information on this paper summarizes recent developments.

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Walknet comprises an artificial neural network that allows for the simulation of a considerable amount of behavioral data obtained from walking and standing stick insects. It has been tested by kinematic and dynamic simulations as well as on a number of six-legged robots. Over the years, various different expansions of this network have been provided leading to different versions of Walknet. This review summarizes the most important biological findings described by Walknet and how they can be simulated. Walknet shows how a number of properties observed in insects may emerge from a decentralized architecture. Examples are the continuum of so-called “gaits,” coordination of up to 18 leg joints during stance when walking forward or backward over uneven surfaces and negotiation of curves, dealing with leg loss, as well as being able following motion trajectories without explicit precalculation. The different Walknet versions are compared to other approaches describing insect-inspired hexapod walking. Finally, we briefly address the ability of this decentralized reactive controller to form the basis for the simulation of higher-level cognitive faculties exceeding the capabilities of insects.