A hexapod walker using a heterarchical architecture for action selection

Malte Schilling,Josef Schmitz,Thierry Hoinville,Axel Schneider,Holk Cruse,Arne Hüffmeier,Jan Paskarbeit

doi:10.3389/fncom.2013.00126

Malte Schilling, Josef Schmitz + Show 5 more

Open Access

https://doi.org/10.3389/fncom.2013.00126

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Abstract

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.

Highlights

In 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
We show six examples of walking situations for which various combinations of active motivation units are required as are different walking velocities, “uncomfortable” starting configurations, or curve walking
In the supplement we provide videos showing the behavior of the robot, the temporal development of the footfall patterns, as well as the temporal sequence of the internal states, represented by the activation of all motivation units

Summary

Introduction

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. 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” (Anderson, 2010) In this way, different procedures might be developed that serve the same or very much related purposes, leading to redundant structures. Flash and Hochner (2005) have reviewed results on both vertebrates and invertebrates that lead these authors to the interpretation that “many different movements can be derived from a limited number of stored primitives.” These movements can further be “combined through a well defined syntax of action to form more complex action.”

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