Unsupervised Emergence of Spatial Structure from Sensorimotor Prediction

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 that space's structure induces 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. Space appears to be a pervasive concept in our perception of the world, and as such plays a central role in most artificial perception systems, in particular in computer vision and robotics applications. Yet its fundamental nature and the mechanisms that could lead to its emergence in an artificial system still remain poorly understood (Kant, 1998; Poincaré, 1895; Nicod, 1924). In most cases, the problem is circumvented by implementing prior knowledge in the system regarding the structure of space, and how motor and sensory information convey spatial properties (for instance through a kinematics model (Siciliano & Khatib, 2016), or a sensor model (Cadena et al., 2016)). In more recent years, and with the developments of machine learning techniques, approaches with less handengineered priors developed to solve spatial tasks ((Kahn et al., 2017; Quillen et al., 2018; Levine et al., 2018; Smolyanskiy et al., 2017) to name a few). However they tend to rule out the specificity of spatial experiences in favor of a global assessment of the agent's performance, leaving the question of the origin and structure of spatial knowledge largely open.