Learning the symbolic representation of tasks enables the application of classical AI search techniques to find a solution in the symbolic definition of the task. For unknown, unstructured, and/or changing environments, it is desirable that the robot itself discovers the symbolic structures that are useful in reasoning and planning. In this talk, I will introduce our recent work, DeepSym, which aims to learn symbols with deep networks from unsupervised robot interactions and build rules defined over these symbols for domain-independent planning. I will continue with follow-up works that extend DeepSym to learn multi-object and relational symbols. I will conclude with a discussion on why an open-ended learning approach would be practical in symbol learning for truly intelligent robots.