Unsupervised Emergence of Spatial Structure from Sensorimotor Prediction
This addresses the fundamental issue of spatial representation in robotics, offering a novel unsupervised approach that could enhance autonomous navigation and perception.
The paper tackled the problem of how spatial knowledge emerges in autonomous agents by proposing a sensorimotor predictive scheme, showing that a naive agent can capture the topology and metric regularity of its spatial configuration without prior knowledge or supervision.
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.