Co-diagnosing configuration failures in co-robotic systems

Abstract

Robotic systems often have complex configuration spaces that, when poorly set, can cause failures. In this work we take advantage of the close synergy between user and robot in co-robotic systems to better diagnose and overcome configuration failures. We leverage users' understanding of the system to mark failures they observe while the system is in operation. A marked failure indicates that the robot either “did not do something when it should have” or “did something when it should not have”. The failure marking is coupled with an automated analysis approach that identifies code predicates involving configuration parameters that may be relevant to each failure type, ranks the parameters according to their potential to be associated with the failure, and suggests adjustments based on the run-time outcome of those predicates. We present the approach, its implementation, and a preliminary study on a configurable unmanned air system. The results show how the approach can successfully help diagnose and adjust faulty configuration parameters in co-robotic systems.