The Logic of Robotics Inspired Biology

Abstract. Biologically inspired robotics is a well known approach for the design of autonomous intelligent robot systems. Very often it is assumed that biologically inspired models successfully imple-mented on robots offer new scientific knowledge for biology too. In other words, robots experiments serving as a replacement for the bio-logical system under investigation are assumed to provide new scien-tific knowledge for biology. This article is a critical investigation of this assumption. We begin by clarifying what we mean by “new sci-entific knowledge. ” Following Karl Popper’s work the The Logic of Scientific Discovery we conclude that in general robotic experiments serving as replacement for biological systems can never directly de-liver any new scientific knowledge for biology. We further argue that there is no formal guideline which defines the level of “biological plausibility ” for biologically inspired robot implementations. There-fore, there is no reason to prefer some kind of robotic setup before others. Any claimed relevance for biology, however, is only justified if results from robotic experiments are translated back into new mod-els and hypotheses amenable to experimental tests within the domain of biology. This translation “back ” into biology is very often missing and we will discuss popular robotics frameworks in the context of Brain Research, Cognitive Science and Developmental Robotics in order to highlight this issue. Nonetheless, such frameworks are valu-able and important, like pure mathematics, because they might lead to new formalisms and methods which in future might be essential for gaining new scientific knowledge if applied in biology. No one can tell, if and which of the current robotics frameworks will provide these new scientific tools. What we can already say–the main mes-sage of this article–is that robot systems serving as a replacement for biological systems won’t be sufficient for the test of biological models, i.e. gaining new scientific knowledge in biology.