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Traversing seen and unseen corridors with Artificial Neural Networks/Context matching

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Titel Traversing seen and unseen corridors with Artificial Neural Networks/Context matching
Autor Narayanan, Krishna Kumar
Posada, Luis-Felipe
Hoffmann, Frank
Bertram, Torsten
Lizenz CC-Namensnennung 3.0 Unported:
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DOI 10.5446/15422
Herausgeber TU Dortmund, Lehrstuhl für Regelungssystemtechnik
Erscheinungsjahr 2011
Sprache Stummfilm
Produktionsjahr 2011
Produktionsort Dortmund

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Abstract Proof of concept experiments to navigate a corridor environment using Two level feature matching architecture. Layer 1 classifies the scenario from the shape and appearance of the environment into Corridor (C) or Open room (O) or Cluttered (L) environment. Layer 2 deploys scenario specific model to predict action and correspondingly navigate the environment. The two models used are: 1. Artificial Neural Networks and 2. Context matching and prediction. Timeline: 0:00 Scenario: Known corridor with Artificial Neural Networks 0:17 Scenario: Known corridor with Context matching and prediction 0:39 Scenario: Unknown corridor with Artificial Neural Networks 1:02 Scenario: Unknown corridor with Context matching and prediction 1:18 Scenario: Transition between trained and an untrained corridor using Artificial Neural Networks This video is the supplement to the paper: "Scenario and context specific visual robot behavior learning" presented at the 2011 IEEE International Conference on Robotics and Automation (ICRA2011), May 9-13 2011, Shanghai International Convention Center, Shanghai, China. For more information please visit: http://www.rst.e-technik.tu-dortmund....
Schlagwörter navigation
gaussian mixture model
learning from demonstration
visual behaviors
obstacle avoidance
homing

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