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.

机器人在组件故障的情况下需要调整其行为，以便在降低能力的条件下实现仍然可达的目标。我们在马尔可夫决策过程（MDP）框架中提出了在提前已知易受故障的执行机构的情况下进行规划的问题。该模型捕捉了基于利用驱动的故障和状态 - 动作相关的执行机构故障概率，以便推理潜在损伤和降低未来控制的长期影响。这导致了与忽略故障的计划有质的差异的行为。随着执行机构的故障，可能会出现组合数目众多的配置。我们识别出通过重用来节省计算的机会，利用不同配置产生密切相关问题的观察结果。我们的结果展示了如何获得战略解决方案，使机器人在故障发生时能够做出响应，例如谨慎地安排利用率以保留关键的执行机构。