Toward a Memory Model for Autonomous Topological Mapping and Navigation: the Case of Binary Sensors and Discrete Actions
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Electrical and Computer Engineering
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Systems Engineering
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We propose a self-organizing database for per- ceptual experience capable of supporting autonomous goal- directed planning. The main contributions are: (i) a formal demonstration that the database is complex enough in principle to represent the homotopy type of the sensed environment; (ii) some initial steps toward a formal demonstration that the database offers a computationally effective, contractible approximation suitable for motion planning that can be ac- cumulated purely from autonomous sensory experience. The provable properties of an effectively trained data-base exploit certain notions of convexity that have been recently generalized for application to a symbolic (discrete) representation of subset nesting relations. We conclude by introducing a learning scheme that we conjecture (but cannot yet prove) will be capable of achieving the required training, assuming a rich enough exposure to the environment. For more information: Kod*Lab
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BibTeX entry INPROCEEDINGS{memory_Guralnik_Koditschek_Allerton_2012, author={Guralnik, D.P. and Koditschek, D.E.}, booktitle={Communication, Control, and Computing (Allerton), 2012 50th Annual Allerton Conference on}, title={Toward a memory model for autonomous topological mapping and navigation: The case of binary sensors and discrete actions}, year={2012}, pages={936-945}, keywords={SLAM (robots);learning (artificial intelligence);mobile robots;navigation;path planning;autonomous goal directed planning;autonomous sensory experience;autonomous topological mapping;binary sensor;discrete action;learning scheme;memory model;motion planning;navigation;self-organizing database;symbolic representation;Computational modeling;Databases;Navigation;Planning;Robot sensing systems;Trajectory}, doi={10.1109/Allerton.2012.6483319}, } Funded in part by the Air Force Office of Science Research under the MURI FA9550–10–1−0567 and in part by the National Science Foundation under CDI-II- 1028237.