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