Automated damage diagnosis and recovery for remote robotics

Remote robotics applications, such as space exploration or operation in hazardous environments, would greatly benefit from automated recovery algorithms for unanticipated failure or damage. In this paper a two-stage evolutionary algorithm is introduced-which we call the estimation-exploration algorithm-that forwards this aim by first evolving a damage hypothesis after failure and then re-evolving a compensatory neural controller to restore functionality. The algorithm presupposes that a robot simulator is running continuously onboard the physical robot. In this paper, the ´physical´ robot is also simulated, but in future work the algorithm will be applied to a real, physical robot. Although evolutionary algorithms require a large number of evaluations to produce a useful solution, the results reported here indicate that almost complete functionality can be restored after only three evaluations on the ´physical´ robot, as opposed to over 3000 evaluations if the compensatory controller is evolved all on the ´physical´ robot. Our algorithm also has the benefit of producing a diagnostic model of the failure.