neuroWalknet, a controller for hexapod walking allowing for context dependent behavior

Abstract

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.

Self‐Organized Stick Insect‐Like Locomotion under Decentralized Adaptive Neural Control: From Biological Investigation to Robot Simulation

“…However, on the other hand, this control method adapts immediately to local disturbances and different gaits. [ 51,52 ]…”

“…However, on the other hand, this control method adapts immediately to local disturbances and different gaits. [51,52] Instead of employing two separate swing and stance networks with an extra selector network and behavior control rules as in WalkNet control, both complex swing and stance profiles can be encoded using a single premotor network. Different premotor network models for this purpose have been introduced, for instance, a recurrent premotor neural network model with long short-term memory (LSTM) activation functions, [96] a feedforward premotor neural network model with hyperbolic tangent activation functions (tanh), [68,97] radial basis or Gaussian activation functions (RBF) [70,73,98] (as also used here), or a deep premotor neural network model with tanh activation functions.…”

“…Therefore, the robot can become unstable if its legs are amputated since parts of the body may drop to the ground. Furthermore, climbing over an obstacle and walk-ing on uneven terrain with a high level of roughness, where the difference between the maximum and minimum heights of the terrain surface is higher than the robot swing amplitude, will require additional control mechanisms, like local leg extension and elevation control [68,100] or intraleg control of neuroWalknet [51,52] with additional sensory feedback.…”

“…The control of locomotion is highly adaptive in stick insects [10,[50][51][52] and partially based on load-controlled proprioception. Studies on the neural control of motion in stick insects with thoracic connective lesions have shown that, in principle, cut thoracic connectives had an effort on the leg coordination of unrestrained walking stick insects.…”

“…[53,54] This distributed (decentralized) control architecture enables adaptive control based on feedback from the legs themselves [55,56] and facilitates a smooth transition between the different walking gait patterns required in the respective situations. [50][51][52]57,58] Different approaches have made use of the knowledge obtained on the integration of stimuli for locomotion control from stick insects (reviewed in ref. [10]).…”