Learning guided planning for robust task execution in cognitive robotics

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.