Department of Computer, Control and Management Engineering

July 30, 11:00 room A6 @ DIS

Actively Using Vision and Context for Home Robotics
Jim Little
University of British Columbia

Increasingly we want computers and robots to observe us and know who we are and what we are doing, and to understand the objects and tasks in our world, both at work and in the home. I will describe how we've built systems for mobile robots to find objects using visual cues and learn about shared workspaces. Further I will review how a range of visual capabilities permits the robot to work for and with humans.

We've demonstrated these abilities on Curious George, our visually-guided mobile robot that has competed and won the Semantic Robot Vision Challenge at AAAI (2007), CVPR (2008) and ISVC (2009), in a completely autonomous visual search task. In the SRVC visual classifiers are learned from images gleaned from the Web. Challenges include poor image quality, badly labeled data and confusing semantics (e.g., synonyms). Clustering of training data, image quality analysis, and viewpoint-guided visual attention enable effective object search by a home robot.