A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2016; you can also visit the original URL.
The file type is
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 anddoi:10.1109/iros.2007.4398978 dblp:conf/iros/Kira07 fatcat:v6eudsw3uzadlcganl5pchs5gy