Spatial Representation and Navigation in a Bio-inspired Robot
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Animals behave in different ways depending on the specific task they are required to solve. In certain cases, if a cue marks the goal location, they can rely on simple stimulusresponse associations. In contrast, other tasks require the animal to be endowed ...
Active vision may be useful to perform landmark-based navigation where landmark relationship requires active scanning of the environment. In this article we explore this hypothesis by evolving the neural system controlling vision and behavior of a mobile r ...
This thesis is about topological navigation, more precisely about space representation, perception, localization and mapping. All these elements are needed in order to obtain a robust and reliable framework for navigation. This is essential in order to mov ...
Navigation is defined as the capability of planning and performing a path from the current position towards a desired location. Different types, or strategies, of navigation are used by animals depending on the task they are trying to solve. Visible goals ...
Emerging models for context-aware role-based access control pose challenging requirements over policy administration. In this paper we address the issues raised by the decentralized administration of a spatially-aware access control model in a mobile setti ...
Different neural systems are involved in animal navigation depending on the type of task. Experimental studies support the idea that the hippocampus is necessary to learn a spatial representation required to navigate toward hidden goals (place response), w ...
Mobile robots are gradually appearing in our daily environments. In order to autonomously navigate in real world environments and interact with objects and humans, robots are facing various major technological challenges. Among the required key competences ...
Robots are rapidly evolving from factory workhorses to robot-companions. The future of robots, as our companions, is highly dependent on their abilities to understand, interpret and represent the environment in an efficient and consistent fashion, in a ...
We present a model for the self-organized formation of place cells, head-direction cells, and spatial-view cells in the hippocampal formation based on unsupervised learning on quasi-natural visual stimuli. The model comprises a hierarchy of Slow Feature An ...
A computational model of the hippocampal function in spatial learning is presented. A spatial representation is incrementally acquired during exploration. Visual and self-motion information is fed into a network of rate-coded neurons. A consistent and stab ...