What’s Next: Recruitment of a Grounded Predictive Body Model for Planning a Robot’s Actions

Holk Cruse,Malte Schilling

doi:10.3389/fpsyg.2012.00383

Abstract

Even comparatively simple, reactive systems are able to control complex motor tasks, such as hexapod walking on unpredictable substrate. The capability of such a controller can be improved by introducing internal models of the body and of parts of the environment. Such internal models can be applied as inverse models, as forward models or to solve the problem of sensor fusion. Usually, separate models are used for these functions. Furthermore, separate models are used to solve different tasks. Here we concentrate on internal models of the body as the brain considers its own body the most important part of the world. The model proposed is formed by a recurrent neural network with the property of pattern completion. The model shows a hierarchical structure but nonetheless comprises a holistic system. One and the same model can be used as a forward model, as an inverse model, for sensor fusion, and, with a simple expansion, as a model to internally simulate (new) behaviors to be used for prediction. The model embraces the geometrical constraints of a complex body with many redundant degrees of freedom, and allows finding geometrically possible solutions. To control behavior such as walking, climbing, or reaching, this body model is complemented by a number of simple reactive procedures together forming a procedural memory. In this article, we illustrate the functioning of this network. To this end we present examples for solutions of the forward function and the inverse function, and explain how the complete network might be used for predictive purposes. The model is assumed to be “innate,” so learning the parameters of the model is not (yet) considered.

Highlights

The capability of reacting to actual stimuli, and predicting future stimuli, was for a long time attributed to “higher animals” and tightly connected to properties of vertebrate brains
In the step we will extend this network toward a model of the whole body, showing how different levels of representations can be integrated and how the model mediates between the different partial models. We show how this complete model can be applied in motor control and how a leg model can be utilized for the inverse model function in this task
The second simulation demonstrates how the same network can be used in motor control to make targeted movements

Summary

Introduction

The capability of reacting to actual stimuli, and predicting future stimuli, was for a long time attributed to “higher animals” and tightly connected to properties of (some) vertebrate brains. Not even “simple” animals like insects are considered merely reactive; it is known that they are able to anticipate future situations. Examples include the prediction of the future position of a moving object, which can be used to visually pursue or reach for it, and the estimation of the mass of an object to be lifted. To allow for such prediction, internal models of the environment are required. Internal models refer to objects in the external environment, and have to include a simulation of – at least parts of – the body

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