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Autonomous mobile robots (AMR) operating in the real world often need to makecritical decisions that directly impact their own safety and the safety oftheir surroundings. Learning-based approaches for decision making have gainedpopularity in recent years, since decisions can be made very quickly and withreasonable levels of accuracy for many applications. These approaches, however,typically return only one decision, and if the learner is poorly trained orobservations are noisy, the decision may be incorrect. This problem is furtherexacerbated when the robot is making decisions about its own failures, such asfaulty actuators or sensors and external disturbances, when a wrong decisioncan immediately cause damage to the robot. In this paper, we consider this verycase study: a robot dealing with such failures must quickly assessuncertainties and make safe decisions. We propose an uncertainty awarelearning-based failure detection and recovery approach, in which we leverageDecision Tree theory along with Model Predictive Control to detect and explainwhich failure is compromising the system, assess uncertainties associated withthe failure, and lastly, find and validate corrective controls to recover thesystem. Our approach is validated with simulations and real experiments on afaulty unmanned ground vehicle (UGV) navigation case study, demonstratingrecovery to safety under uncertainties.

A Decision Tree-based Monitoring and Recovery Framework for Autonomous Robots with Decision Uncertainties