In this work, we researched methods of maintaining flexible models of robot capabilities in order to detect changes or faults. For example, we were able to detect faults such as a loose wheel on a Pioneer robot or a tilted camera. We have devised a complete framework that takes in information about what sensors the robot has and what sensor processing occurs, and automatically find stable mappings that occur while performing a task.
The approach we took was one of learning correlations between sensing at multiple levels of abstraction during normal operation. For example, we correlate not just odometry and sonar data, but also optic flow data processed from a camera image. We also use unsupervised learning to model the correlations to avoid having to model individual fault types beforehand, since they might not be known.