  Despite its omnipresence in robotics application, the nature of spatial
knowledge and the mechanisms that underlie its emergence in autonomous agents
are still poorly understood. Recent theoretical work suggests that the concept
of space can be grounded by capturing invariants induced by the structure of
space in an agent's raw sensorimotor experience. Moreover, it is hypothesized
that capturing these invariants is beneficial for a naive agent trying to
predict its sensorimotor experience. Under certain exploratory conditions,
spatial representations should thus emerge as a byproduct of learning to
predict. We propose a simple sensorimotor predictive scheme, apply it to
different agents and types of exploration, and evaluate the pertinence of this
hypothesis. We show that a naive agent can capture the topology and metric
regularity of its spatial configuration without any a priori knowledge, nor
extraneous supervision.
