  Robots incurring component failures ought to adapt their behavior to best
realize still-attainable goals under reduced capacity. We formulate the problem
of planning with actuators known a priori to be susceptible to failure within
the Markov Decision Processes (MDP) framework. The model captures
utilization-driven malfunction and state-action dependent likelihoods of
actuator failure in order to enable reasoning about potential impairment and
the long-term implications of impoverished future control. This leads to
behavior differing qualitatively from plans which ignore failure. As actuators
malfunction, there are combinatorially many configurations which can arise. We
identify opportunities to save computation through re-use, exploiting the
observation that differing configurations yield closely related problems. Our
results show how strategic solutions are obtained so robots can respond when
failures do occur -- for instance, in prudently scheduling utilization in order
to keep critical actuators in reserve.
