Decentralized control has been established as a key control principle in insect walking and has been successfully leveraged to account for a wide range of walking behaviors in the proposed neuroWalknet architecture. This controller allows for walking patterns at different velocities in both, forward and backward direction-quite similar to the behavior shown in stick insects-, for negotiation of curves, and for robustly dealing with various disturbances. While these simulations focus on the cooperation of different, decentrally controlled legs, here we consider a set of biological experiments not yet been tested by neuroWalknet, that focus on the function of the individual leg and are context dependent. These intraleg studies deal with four groups of interjoint reflexes. The reflexes are elicited by stimulation of the femoral chordotonal organ (fCO) or groups of campaniform sensilla (CS). Motor output signals are recorded from the alpha-joint, the beta-joint or the gamma-joint of the leg. Furthermore, the influence of these sensory inputs to artificially induced oscillations by application of pilocarpine has been studied. Although these biological data represent results obtained from different local reflexes in different contexts, they fit with and are embedded into the behavior shown by the global structure of neuroWalknet. In particular, a specific and intensively studied behavior, active reaction, has since long been assumed to represent a separate behavioral element, from which it is not clear why it occurs in some situations, but not in others. This question could now be explained as an emergent property of the holistic structure of neuroWalknet which has shown to be able to produce artificially elicited pilocarpine-driven oscillation that can be controlled by sensory input without the need of explicit innate CPG structures. As the simulation data result from a holistic system, further results were obtained that could be used as predictions to be tested in further biological experiments.

Locomotion in animals provides a model for adaptive behavior as it is able to deal with various kinds of perturbations. Work in insects suggests that this evolved flexibility results from a modular architecture, which can be characterized by a recurrent neural network allowing for various emerging attractor states. Whereas a lower control-level coordinates joint movements on a short timescale, a higher-level handles action selection on longer timescales. Implementation of such a control system on a walking hexapod robot was able to deal with various walking patterns including disturbances such as uneven terrain or loss of a leg. Here, we propose a cognitive expansion to the adaptive control system that allows dealing with novel challenging situations. This approach makes use of an internal simulation-based planner that is triggered when the model-free controller fails to recover from an unstable pose. Using a grounded internal body model, the planner then tries, in internal simulation, different solutions out of context, and thus, proposes a new plan to be executed on the real robot. We demonstrate the feasibility of this control approach for walking over terrain with uncertain footholds in three scenarios.

Controlling the six legs of an insect walking in an unpredictable environment is a challenging task, as many degrees of freedom have to be coordinated. Solutions proposed to deal with this task are usually based on the highly influential concept that (sensory-modulated) central pattern generators (CPG) are required to control the rhythmic movements of walking legs. Here, we investigate a different view. To this end, we introduce a sensor based controller operating on artificial neurons, being applied to a (simulated) insectoid robot required to exploit the "loop through the world" allowing for simplification of neural computation. We show that such a decentralized solution leads to adaptive behavior when facing uncertain environments which we demonstrate for a broad range of behaviors never dealt with in a single system by earlier approaches. This includes the ability to produce footfall patterns such as velocity dependent "tripod", "tetrapod", "pentapod" as well as various stable intermediate patterns as observed in stick insects and in Drosophila. These patterns are found to be stable against disturbances and when starting from various leg configurations. Our neuronal architecture easily allows for starting or interrupting a walk, all being difficult for CPG controlled solutions. Furthermore, negotiation of curves and walking on a treadmill with various treatments of individual legs is possible as well as backward walking and performing short steps. This approach can as well account for the neurophysiological results usually interpreted to support the idea that CPGs form the basis of walking, although our approach is not relying on explicit CPG-like structures. Application of CPGs may however be required for very fast walking. Our neuronal structure allows to pinpoint specific neurons known from various insect studies. Interestingly, specific common properties observed in both insects and crustaceans suggest a significance of our controller beyond the realm of insects.

While internal models are recruited in many tasks and can subserve in this way perception and cognition, it is important that they are grounded and embodied in sensorimotor representation. In this paper we analyze an internal model of the body and show how it can be used for motor control. We extend the Mean of Multiple Computation principle to a dual quaternion representation of transformation and show how this can be directly applied to the control of a simulated robot leg. The model is encoded as a recurrent neural network acting as an autoassociator that is able to solve any kinematic problem in an iterative fashion. We will analyze the convergence properties, especially when additional constraints (acting on the joint level) are introduced that restrict the attractor space.

While internal models are a prerequisite for higher-level function, they have to be grounded in lower-level function serving sensorimotor control. In this paper we introduce an internal body model for the control of a hexapod walker. The internal model deals with a highly complex robotic structure of 22 degrees of freedom and coordinates the single joint movements to achieve an overall stable and adaptive walking behavior. It is implemented as a hierarchical recurrent neural network consisting of different levels of abstraction which are tightly intertwined. We demonstrate the feasibility of the concept by applying the model to a simulated robot and show how the different levels of the body model interact and how this allows to scale the model even further. While the internal model is used in this context explicitly for motor control, it is also a predictive model and can be applied for sensor fusion. We discuss how in this way such an internal model offers the flexibility to be utilized in motor control and to be used for planning ahead by a cognitive expansion of the movement controller.

In many animals the ability to navigate over long distances is an important prerequisite for foraging. For example, it is widely accepted that desert ants and honey bees, but also mammals, use path integration for finding the way back to their home site. It is however a matter of a long standing debate whether animals in addition are able to acquire and use so called cognitive maps. Such a 'map', a global spatial representation of the foraging area, is generally assumed to allow the animal to find shortcuts between two sites although the direct connection has never been travelled before. Using the artificial neural network approach, here we develop an artificial memory system which is based on path integration and various landmark guidance mechanisms (a bank of individual and independent landmark-defined memory elements). Activation of the individual memory elements depends on a separate motivation network and an, in part, asymmetrical lateral inhibition network. The information concerning the absolute position of the agent is present, but resides in a separate memory that can only be used by the path integration subsystem to control the behaviour, but cannot be used for computational purposes with other memory elements of the system. Thus, in this simulation there is no neural basis of a cognitive map. Nevertheless, an agent controlled by this network is able to accomplish various navigational tasks known from ants and bees and often discussed as being dependent on a cognitive map. For example, map-like behaviour as observed in honey bees arises as an emergent property from a decentralized system. This behaviour thus can be explained without referring to the assumption that a cognitive map, a coherent representation of foraging space, must exist. We hypothesize that the proposed network essentially resides in the mushroom bodies of the insect brain.

How is it possible for an autonomous agent to learn and retrieve hierarchically organized information? The question is particularly interesting if there is not a simple, tree-like hierarchy, but when low-level items may belong to several superordinate elements. In this article we propose a solution for this problem following the ideas of O'Connor et al., which are guided by the observation that children learn superordinate concepts from implicitly given information. For the simulation, we use a simple, one-layered RNN consisting of IC units and a very simple learning rule based on teacher forcing. We assume the capability of figure-ground separation as being given. Furthermore, the agent is assumed to owe sensory systems. There are, however, no explicit, preformulated concepts given like `Red'. Further, the agent is assumed to be able to record words. Each object presented to the agent is separately and individually learned and represented in the RNN forming an episodic memory. We show that, using this simple approach, learning and retrieval of hierarchically organized information is possible, although this information is not given explicitly and no hierarchical structure can be found in the network. Learning is very fast. The net shows top-down generalisation and bottom-up activation. Furthermore, asymmetric priming effects can be observed similar to those fond in human subjects. The agent is able to chunk different sensory inputs to represent the same object in memory, but nevertheless being able to distinguish between the different stimuli if, during learning, a supervisor labels the different stimuli with the same name.

Walknet, a biological inspired neural network, has been proposed to control walking of a 6-legged; 18 DoF walker. Control of stance movement is difficult because all legs in stance are mechanically coupled. Based on the idea of velocity feedback, recently a specific revision, local positive velocity feedback, has been proposed to solve this problem using an extremely decentralised architecture. In this control scheme the locally measured joint velocity of an elastic joint is fed into the same joint in the next time step to maintain the movement. At the same time an additional part of the same joint controller observes the mechanical joint power to confine the positive feedback. In this paper, we show that this principle can actually be successfully applied to move a complete 6-legged system. This is tested using a dynamic simulation.

A cognitive control system for a walking robot should be able to solve from simple reactive tasks up to complex tasks, including tasks which need cognitive capabilities and setting up plans. Planning ahead involves some kind of internal representation: most important a model of the own body. Considering planning as mental simulation, this model must be fully functional: it is constrained in the same way as the body itself and it can move and be used in the same way as the body. This model can then be used to try out movements mentally without doing the action in reality. For this purpose it must be possible to decouple the body itself from the action controlling modules to use the original controllers for control of the internal representations. In this publication we introduce a hierarchical model, implemented as an recurrent neural network based on the MMC principle.

What enables an organism to perform behaviour we would call cognitive and adaptive, like language? Here, it is argued that an essential prerequisite is the ability to build up mental representations of external situations to uncouple the behaviour from direct environmental control. Such representations can be realized by building up cell assemblies. The recurrent neural network presented to cope with this task has been used for generation of action but can also be utilized as a basis for mental representations due to its attractor characteristics. In this context, a new learning algorithm (Dynamic Delta Rule) is proposed, which leads to a self-organized weight distribution yielding stable states on the one hand and which, on the other hand, only activates subpopulations of larger networks that code for the respective situation. In a second step, ways are shown of how the static information of these internal models can be transformed into time-dependent behavioural sequences.

Starting from studies which revealed that positive feedback is found in the control system for walking in arthropods, we have constructed a new positive feedback driven joint that can be used for solving compliant motion tasks. We propose two different joint constructions each of which shows passive compliance. Based on these joints we introduce three different local positive velocity feedback (LPVF) controllers and discuss their properties in the context of motion generation in closed kinematic chains. The third circuit named undelayed dLPVF is used for the control of a compliant planar manipulator which turns a crank. Our concept is of highly decentralized nature and follows the idea of embodiment. In our case this means that a process which is controlled by LPVF controllers reveals its nature when the controllers interact with this process.