In the field of domestic service robots, recovery from faults is crucial to promote user acceptance. In this context we focus in particular on some specific faults, which arise from the interaction of a robot with its real world environment. Even a well-modelled robot may fail to perform its tasks successfully due to unexpected situations, which occur while interacting. These situations occur as deviations of properties of the objects (manipulated by the robot) from their expected values. Hence, they are experienced by the robot as external faults. In this paper we present two approaches to handle external faults which result from inadequate descriptions of a planner operator. In both approaches we assume that the robot is able to detect the occurrence of the fault at the planning level by monitoring the effects of an executed action. In our work we limit the scope of the sources of external faults to natural physical phenomena. Hence, we do not consider cases in which an external agent (e.g. another robot, a human being) is the cause of a detected fault. We apply the proposed approaches to scenarios in which the robot performs a manipulation task (pick and place).

A cognitive robot may face failures during the execution of its actions in the physical world. In this paper, we investigate how robots can ensure robustness by gaining experience on action executions, and we propose a lifelong experimental learning method. We use Inductive Logic Programming (ILP) as the learning method to frame new hypotheses. ILP provides first-order logic representations of the derived hypotheses that are useful for reasoning and planning processes. Furthermore, it can use background knowledge to represent more advanced rules. Partially specified world states can also be easily represented in these rules. All these advantages of ILP make this approach superior to attribute-based learning approaches. Experience gained through incremental learning is used as a guide to future decisions of the robot for robust execution. The results on our Pioneer 3DX robot reveal that the hypotheses framed for failure cases are sound and ensure safety in future tasks of the robot.