A cognitive robot may face several types of 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 to derive new hypotheses. Our proposed learning process takes into account the actions, the objects in interest and their relations to guide the robot's future decisions. We use Inductive Logic Programming as the learning method to frame hypotheses for both efficient execution types and failure situations. ILP learning provides first-order logical representations of the derived hypotheses that are useful for reasoning and planning processes. Experience gained through incremental learning is used as a guide to the future decisions of the robot for robust execution. In the experiments, the performance of ILP learning is analysed on a Pioneer 3DX robot with comparison to attribute-based learners. The results reveal that the hypotheses framed for failure cases are sound and ensure safety in future tasks of the robot.