• Corpus ID: 5723058

View-based cognitive map learning by an autonomous robot

@inproceedings{Mallot1995ViewbasedCM,
  title={View-based cognitive map learning by an autonomous robot},
  author={Hanspeter A. Mallot and Heinrich H. B{\"u}lthoff and Philip Georg and Bernhard Sch{\"o}lkopf and Kenji Yasuhara and Françoise Fogelman-Souli{\'e}},
  year={1995}
}
This paper presents a view based approach to map learning and nav igation in mazes By means of graph theory we have shown that the view graph is a su cient representation for map behaviour such as path planning A neural network for unsupervised learning of the view graph from sequences of views is constructed We use a modi ed Kohonen learning rule that transforms temporal sequence rather than fea tural similarity into connectedness In the main part of the paper we present a robot implementation… 
Recognition-Triggered Response and the View-Graph Approach to Spatial Cognition
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Psychophysical evidence from experiments using virtual reality indicating that human subjects do make use of simple view-movement associations without recognizing places is presented, indicating that the view-graph is less computationally expensive and can easily be adapted to the coarseness of spatial knowledge.
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A purely vision-based scheme for learning a topological representation of an open environment that represents selected places by local views of the surrounding scene, and finds traversable paths between them, and can be performed without using metric information.
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This paper proposes a connectionist model to learn a spatial representation of the world based on temporal memory of perceptions and actions of the robot. It is constructed at run-time to merge the
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A new approach to segmentation in a mobile robot by a modular network SOM (mnSOM) is proposed, and Hidden Markov models (HMMs) are used instead of a deterministic method to form a graph-based map for efficient processing.
Neural Network Architecture for Cognitive Navigation in Dynamic Environments
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The proposed architecture provides the robot with cognitive abilities and enables reliable and flexible navigation in realistic time-evolving environments and it is proved that the subconscious pathway is robust against uncertainty in the sensory information.
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It is argued that the graph of local views (snapshots) is a general and biologically plausible means of representing space and integrating the various mechanisms of map behaviour.
Context-Based Cognitive Map Learning for an Autonomous Robot Using a Model of Cortico-Hippocampal Interplay
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This paper presents an additional module of cortico-hippocampal interplay to the view-based competitive sequence learning scheme of spatial memory by use of a middle-term memory module as a model of hippocampal function.
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References

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A neural network is presented that learns the view graph during a random exploration of the maze using an unsupervised competitive learning rule translating temporal sequence of views into connectedness in the network, improving the view-recognition performance.
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A robust, qualitative a p p r o a c h to a spatial learning mobile robot
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