Modeling cross-sensory and sensorimotor correlations to detect and localize faults in mobile robots

We present a novel framework for learning crosssensory and sensorimotor correlations in order to detect and localize faults in mobile robots. Unlike traditional fault detection and identification schemes, we do not use a priori models of fault states or system dynamics. Instead, we utilize additional information and possible source of redundancy that mobile robots have available to them, namely a hierarchical graph representing stages of sensory processing at multiple levels of abstractions and their outputs. We learn statistical models of correlations between elements in the hierarchy, in addition to the control signals, and use this to detect and identify changes in the capabilities of the robot. The framework is instantiated using Self-Organizing Maps, a simple unsupervised learning algorithm. Results indicate that the system can detect sensory and motor faults in a mobile robot and identify their cause, without using a priori models of the robot or its fault states.