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Maps, Objects and Contexts for Robots

  • James J. Little
  • Tristram Southey
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 8)

When you have brought your new Apple iRobot home for the first time, you are faced with the challenging task of introducing the robot to its new home/workspace. Of course the robot knows about homes and typical tasks. That’s why you bought the sleek, stylish robot, in addition to the fact that it promised a simple interface. It prompts you to take it on a tour of the house, naming the rooms, pointing out the appliances, and identifying the occupants of the house.

The promise of the now discontinued Aibo whose communication and basic behaviours show that even simple visual sensors using strong features (SIFT[17]) can enable visual tracking and recognition. Built in to the home robot will be the necessary concepts – tasks, objects, contexts, locations. Your home vacuum robot “knows” only about stairs, objects, infrared walls, and random search. Your iRobot knows about kitchens, doors, stairs, bedrooms, beer (you have the party version of the iRobot that can bring you beer in the entertainment room). How does the robot tie the sensory flow it receives to its plans, names, and goals in its repertoire?

This fanciful thought experiment is not so far in the future. For some applications such as assistive technologies[21, 1] which operate in contexts where rich visual sensing is deployed, the range of objects may be limited to a care facility where patients’ rooms are typically constrained in their contents. Here it may be effective to learn the connection between features of the visual stream and the object in the scene, and how they influence the actions of the robot.

Keywords

Mobile Robot Assistive Technology Appearance Model Service Robot Stereo Camera 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  • James J. Little
    • 1
  • Tristram Southey
    • 1
  1. 1.Department of Computer ScienceUniversity of British ColumbiaVancouverCanada

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