As robots become increasingly pervasive in human society, there is a need for developing theoretical frameworks for “human–machine shared contexts.” In this chapter, we develop a framework for endowing robots with a human-like capacity for meta-reasoning. We consider the case of an assembly robot that is given a task slightly different from the one for which it was preprogrammed. In this scenario, the assembly robot may fail to accomplish the novel task. We develop a conceptual framework for using meta-reasoning to recover and learn from the robot failure, including a specification of the problem, a taxonomy of failures, and an architecture for meta-reasoning. Our framework for robot learning from failure grounds meta-reasoning in action and perception.