Evaluating Morphological Computation in Muscle and DC-motor Driven Models of Human Hopping

TL;DR

This paper compares two measures of morphological computation in muscle and motor-driven hopping models, revealing that behavior- and state-dependent analysis offers deeper insights than averaged measures alone.

Contribution

It introduces and evaluates two measures of morphological computation, demonstrating the importance of state-dependent analysis in embodied movement models.

Findings

State-dependent analysis provides additional insights.

Measures can be applied to both robotic and biological models.

Algorithms and code are provided for implementation.

Abstract

In the context of embodied artificial intelligence, morphological computation refers to processes which are conducted by the body (and environment) that otherwise would have to be performed by the brain. Exploiting environmental and morphological properties is an important feature of embodied systems. The main reason is that it allows to significantly reduce the controller complexity. An important aspect of morphological computation is that it cannot be assigned to an embodied system per se, but that it is, as we show, behavior- and state-dependent. In this work, we evaluate two different measures of morphological computation that can be applied in robotic systems and in computer simulations of biological movement. As an example, these measures were evaluated on muscle and DC-motor driven hopping models. We show that a state-dependent analysis of the hopping behaviors provides additional insights that cannot be gained from the averaged measures alone. This work includes algorithms and computer code for the measures.