Robots, compute servers, and workstations for semantic robot vision

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Recent advances in visually guided mobile robotics and visual object recognition  promise to enable us to build home robot assistants that can work with people, knowing about the home, the objects in the home, and their use.  At UBC we have developed a mobile robot, Curious George, that can search a small room and photograph objects from a target list.  The robot learns how to identify objects by searching the web, gathering images showing the objects, and then learns how to find the objects in the room.  The robot then maps the room and finds a route that lets it search for the objects, and returns with a set of images showing the objects it has found. Curious George won the recently held Semantic Robot Vision Challenge.More generally, such robots can learn the appearance of classes of objects and then recognize them, sometimes using the context in which they appear.  But these robots need to comb through the images, finding distinctive features of the objects, and build complex classifiers from the images.  The robots themselves need to be agile and compact.We are now applying for new robots and computation servers that will enable us to experiment in realistic environments such as a home.  The servers need substantial memory since the data we must understand for feature learning comprises thousands of images, for example, the record of a robot's experience in the home.  Mapping also stretches the capabilities of computing systems since the robot acquires tens of thousands of landmarks while traversing a home.  Finally, to utilize context in visual search we need to learn the spatial relations between many different objects and their properties.