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Toward a Memory Model for Autonomous Topological Mapping and Navigation:

the Case of Binary Sensors and Discrete Actions

Dan P. Guralnik and Daniel E. Koditschek
Department of Electrical and Systems Engineering, University of Pennsylvania
Full PDF | Penn Scholarly Commons

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.
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.
BibTeX entry
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}, 
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;

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