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 works suggest that the Euclidean structure of space induces invariants in an agent's raw sensorimotor experience. We hypothesize that capturing these invariants is beneficial for sensorimotor prediction and that, under certain exploratory conditions, a motor representation capturing the structure of the external space should emerge as a byproduct of learning to predict future sensory experiences. We propose a simple sensorimotor predictive scheme, apply it to different agents and types of exploration, and evaluate the pertinence of these hypotheses. We show that a naive agent can capture the topology and metric regularity of its sensor's position in an egocentric spatial frame without any a priori knowledge, nor extraneous supervision.
@cite_30: Biological systems such as rats have special brain structures which process spatial information from the environment. They have efficient and robust localization abilities provided by special neurons in the hippocampus, namely place cells. This work proposes a biologically plausible architecture which is based on three recently developed techniques: Reservoir Computing (RC), Slow Feature Analysis (SFA), and Independent Component Analysis (ICA). The bottom layer of our RC-SFA architecture is a reservoir of recurrent nodes which process the information from the robot's distance sensors. It provides a temporal kernel of rich dynamics which is used by the upper two layers (SFA and ICA) to autonomously learn place cells. Experiments with an e-puck robot with 8 infra-red sensors (which measure distances in [4–30] cm) show that the learning system based on RC-SFA provides a self-organized formation of place cells that can either distinguish between two rooms or to detect the corridor connecting them.
@cite_22: A goal of sensory coding is to capture features of sensory input that are behaviorally relevant. Therefore, a generic principle of sensory coding should take into account the motor capabilities of an agent. Up to now, unsupervised learning of sensory representations with respect to generic coding principles has been limited to passively received sensory input. Here we propose an algorithm that reorganizes an agent's representation of sensory space by maximizing the predictability of sensory state transitions given a motor action. We applied the algorithm to the sensory spaces of a number of simple, simulated agents with different motor parameters, moving in two-dimensional mazes. We find that the optimization algorithm generates compact, isotropic representations of space, comparable to hippocampal place fields. As expected, the size and spatial distribution of these place fields-like representations adapt to the motor parameters of the agent as well as to its environment. The representations prove to be well suited as a basis for path planning and navigation. They not only possess a high degree of state-transition predictability, but also are temporally stable. We conclude that the coding principle of predictability is a promising candidate for understanding place field formation as the result of sensorimotor reorganization.
@cite_41: 
@cite_9: 
@cite_40: ▪ Abstract The startling discovery by O'Keefe & Dostrovsky (Brain Res. 1971; 34: 171–75) that hippocampal neurons fire selectively in different regions or “place fields” of an environment and the subsequent development of the comprehensive theory by O'Keefe & Nadel (The Hippocampus as a Cognitive Map. Oxford, UK: Clarendon, 1978) that the hippocampus serves as a cognitive map have stimulated a substantial body of literature on the characteristics of hippocampal “place cells” and their relevance for our understanding of the mechanisms by which the brain processes spatial information. This paper reviews the major dimensions of the empirical research on place-cell activity and the development of computational models to explain various characteristics of place fields.
@cite_23: Abstract The Spatial Semantic Hierarchy is a model of knowledge of large-scale space consisting of multiple interacting representations, both qualitative and quantitative. The SSH is inspired by the properties of the human cognitive map, and is intended to serve both as a model of the human cognitive map and as a method for robot exploration and map-building. The multiple levels of the SSH express states of partial knowledge, and thus enable the human or robotic agent to deal robustly with uncertainty during both learning and problem-solving. The control level represents useful patterns of sensorimotor interaction with the world in the form of trajectory-following and hill-climbing control laws leading to locally distinctive states. Local geometric maps in local frames of reference can be constructed at the control level to serve as observers for control laws in particular neighborhoods. The causal level abstracts continuous behavior among distinctive states into a discrete model consisting of states linked by actions. The topological level introduces the external ontology of places, paths and regions by abduction to explain the observed pattern of states and actions at the causal level. Quantitative knowledge at the control, causal and topological levels supports a “patchwork map” of local geometric frames of reference linked by causal and topological connections. The patchwork map can be merged into a single global frame of reference at the metrical level when sufficient information and computational resources are available. We describe the assumptions and guarantees behind the generality of the SSH across environments and sensorimotor systems. Evidence is presented from several partial implementations of the SSH on simulated and physical robots.
@cite_31: 
@cite_34: We model hippocampal place cells and head-direction cells by combining allothetic (visual) and idiothetic (proprioceptive) stimuli. Visual input, provided by a video camera on a miniature robot, is preprocessed by a set of Gabor filters on 31 nodes of a log-polar retinotopic graph. Unsupervised Hebbian learning is employed to incrementally build a population of localized overlapping place fields. Place cells serve as basis functions for reinforcement learning. Experimental results for goal-oriented navigation of a mobile robot are presented.
@cite_20: 
The problem is often conceptualized as the learning of grid or place cells, inspired by neuroscience @cite_40 . Place cells have been built as a way to compress sensory information @cite_34 , or to improve sensorimotor and reward predictability @cite_22 @cite_20 @cite_31 . Grid cells have been built as an intermediary representations in recurrent networks trained to predict an agent's position @cite_9 @cite_41 . Both place and grid cells have also been extracted by processing the internal state of a reservoir @cite_30 . Representations akin to place cells and displacements have also been built from low-level sensorimotor interaction @cite_23 .