For a robot, an animal, and even for man, to be able to use an internal representation of the spatial layout of its environment to position itself is a very complex task, which raises numerous issues of perception, categorization and motor control that must all be solved in an integrated manner to promote survival. This point is illustrated here, within the framework of a review of localization strategies in mobile robots. The allothetic and idiothetic sensors that may be used by these robots to build internal representations of their environment, and the maps in which these representations may be instantiated, are first described. Then map-based navigation systems are categorized according to a 3-level hierarchy of localization strategies, which respectively call upon direct position inference, single-hypothesis tracking, and multiple-hypothesis tracking. The advantages and drawbacks of these strategies, notably with respect to the limitations of the sensors on which they rely, are discussed throughout the text.

We study spatial learning and navigation for autonomous agents. A state space representation is constructed by unsupervised Hebbian learning during exploration. As a result of learning, a representation of the continuous two-dimensional (2-D) manifold in the high-dimensional input space is found. The representation consists of a population of localized overlapping place fields covering the 2-D space densely and uniformly. This space coding is comparable to the representation provided by hippocampal place cells in rats. Place fields are learned by extracting spatio-temporal properties of the environment from sensory inputs. The visual scene is modeled using the responses of modified Gabor filters placed at the nodes of a sparse Log-polar graph. Visual sensory aliasing is eliminated by taking into account self-motion signals via path integration. This solves the hidden state problem and provides a suitable representation for applying reinforcement learning in continuous space for action selection. A temporal-difference prediction scheme is used to learn sensorimotor mappings to perform goal-oriented navigation. Population vector coding is employed to interpret ensemble neural activity. The model is validated on a mobile Khepera miniature robot.

This paper is complementary to a previous review of signicant research on adaptive behavior in animats. It summarizes the current stateof -the art and outlines directions for possible progress. 1. Introduction In the proceedings of SAB94, we published a review of signicant research on adaptive behavior in animats since the rst SAB conference, held in 1990 (MEYE94). This review summarized the state-of-the art, insofar as the proceedings of three dedicated conferences could help delineate it. Now that three other SAB conferences have been held, we considered that it would be useful to update that earlier review, in order to assess the corresponding progress, to infer the directions in which interesting developments are likely to be expected, and to stress needs for specic additional research eorts. As in the preceding review, this one makes reference only to SAB conference proceedings (SAB96, SAB98, SAB00), on the premise that this perspective, although voluntarily limited, d...

Drawing inspiration from biology, the Psikharpax project aims at endowing a robot with a sensori-motor equipment and a neural control architecture that will afford some of the capacities of autonomy and adaptation that are exhibited by real rats. The paper summarizes the current state of achievement of the project. It successively describes the robot’s future sensors and actuators, and several biomimetic models of the anatomy and physiology of structures in the rat’s brain, like the hippocampus and the basal ganglia, which have already been at work on various robots, and that make navigation and action selection possible. Preliminary results on the implementation of learning mechanisms in these structures are also presented. Finally, the article discusses the potential benefits that a biologically-inspired approach affords to traditional autonomous robotics.

We investigated the importance of hippocampal theta oscillations and the significance of phase differences of theta modulation in the cortical regions that are involved in goal-directed spatial navigation. Our models used representations of entorhinal cortex layer III (ECIII), hippocampus and prefrontal cortex (PFC) to guide movements of a virtual rat in a virtual environment. The model encoded representations of the environment through long-term potentiation of excitatory recurrent connections between sequentially spiking place cells in ECIII and CA3. This encoding required buffering of place cell activity, which was achieved by a short-term memory (STM) in EC that was regulated by theta modulation and allowed synchronized reactivation with encoding phases in ECIII and CA3. Inhibition at a specific theta phase deactivated the oldest item in the buffer when new input was presented to a full STM buffer. A 1808 phase difference separated retrieval and encoding in ECIII and CA3, which enabled us to simulate data on theta phase precession of place cells. Retrieval of known paths was elicited in ECIII by input at the retrieval phase from PFC working memory for goal location, requiring strict theta phase relationships with PFC. Known locations adjacent to the virtual rat were retrieved in CA3. Together, input from ECIII and CA3 activated predictive spiking in cells in CA1 for the next desired place on a shortest path to a goal. Consistent with data, place cell activity in CA1 and CA3 showed smaller place fields than in ECIII.

journal homepage: www.elsevier.com/locate/ynlme A model of episodic memory: Mental time travel along encoded trajectories

After a short review of biologically inspired navigation architectures, mainly relying on modeling the hippocampal anatomy, or at least some of its functions, we present a navigation and planning model for mobile robots. This architecture is based on a model of the hippocampal and prefrontal interactions. In particular, the system relies on the definition of a new cell type “transition cells ” that encompasses traditional “place cells”.