Synthesizing physically-realistic environmental models from robot exploration

Abstract

In previous work [4] a framework was demonstrated that allows an autonomous robot to automatically synthesize physically-realistic models of its own body. Here it is demonstrated how the same approach can be applied to empower a robot to synthesize physically-realistic models of its surroundings. Robots which build numerical or other non-physical models of their environments are limited in the kinds of predictions they can make about the repercussions of future actions. In this paper it is shown that a robot equipped with a self-made, physically-realistic model can extrapolate: a slow-moving robot consistently predicts the much faster top speed at which it can safely drive across a terrain. © Springer-Verlag Berlin Heidelberg 2007.