Despite substantial advances in many different fields of neurorobotics in general, and biomimetic robots in particular, a key challenge is the integration of concepts: to collate and combine research on disparate and conceptually disjunct research areas in the neurosciences and engineering sciences. We claim that the development of suitable robotic integration platforms is of particular relevance to make such integration of concepts work in practice. Here, we provide an example for a hexapod robotic integration platform for autonomous locomotion. In a sequence of six focus sections dealing with aspects of intelligent, embodied motor control in insects and multipedal robots—ranging from compliant actuation, distributed proprioception and control of multiple legs, the formation of internal representations to the use of an internal body model—we introduce the walking robot HECTOR as a research platform for integrative biomimetics of hexapedal locomotion. Owing to its 18 highly sensorized, compliant actuators, light-weight exoskeleton, distributed and expandable hardware architecture, and an appropriate dynamic simulation framework, HECTOR offers many opportunities to integrate research effort across biomimetics research on actuation, sensory-motor feedback, inter-leg coordination, and cognitive abilities such as motion planning and learning of its own body size.

Moving in a cluttered environment with a six-legged walking machine that has additional body actuators, therefore controlling 22 DoFs, is not a trivial task. Already simple forward walking on a flat plane requires the system to select between different internal states. The orchestration of these states depends on walking velocity and on external disturbances. Such disturbances occur continuously, for example due to irregular up-and-down movements of the body or slipping of the legs, even on flat surfaces, in particular when negotiating tight curves. The number of possible states is further increased when the system is allowed to walk backward or when front legs are used as grippers and cannot contribute to walking. Further states are necessary for expansion that allow for navigation. Here we demonstrate a solution for the selection and sequencing of different (attractor) states required to control different behaviors as are forward walking at different speeds, backward walking, as well as negotiation of tight curves. This selection is made by a recurrent neural network (RNN) of motivation units, controlling a bank of decentralized memory elements in combination with the feedback through the environment. The underlying heterarchical architecture of the network allows to select various combinations of these elements. This modular approach representing an example of neural reuse of a limited number of procedures allows for adaptation to different internal and external conditions. A way is sketched as to how this approach may be expanded to form a cognitive system being able to plan ahead. This architecture is characterized by different types of modules being arranged in layers and columns, but the complete network can also be considered as a holistic system showing emergent properties which cannot be attributed to a specific module.Keywords: insect locomotion, motor control, decentral architecture, action selection INTRODUCTIONIn this article, we propose a simple neural architecture that consists of basically independent, parallel sensori-motor procedures-or modules-that allows to orchestrate these modules. In addition this architecture shows the property for easy expansions of the system. This architecture is not based on a specific biological brain structure, but is inspired by behavioral experiments on insects (Cruse et al., 2009a) as are walking on unpredictable environment, performed with stick insects, and navigation, performed with desert ants and honey bees. Nonetheless, such a modular architecture may be of broader interest because many authors assume that a modular structure is a basic property of brains in general.For example, Anderson (2010) has argued that evolution had to find specific solutions for quite different requirements posed by specific environmental conditions as are locomotion, mating, navigation or feeding, and problems occurring later during evolutionary development may be solved by combining existing (functional) modules in different ways, following the principle of "neural reuse" (Ander...

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Abstract. While navigating their environments it is essential for autonomous mobile robots to actively avoid collisions with obstacles. Flying insects perform this behavioural task with ease relying mainly on information the visual system provides. Here we implement a bioinspired collision avoidance algorithm based on the extraction of nearness information from visual motion on the hexapod walking robot platform HECTOR. The algorithm allows HECTOR to navigate cluttered environments while actively avoiding obstacles.