Robotic systems have already found widespread adoption in controlled environments such as manufacturing and logistics, where tasks follow well-defined rules. However, they struggle in unstructured, dynamic, real-world settings that demand adaptability and autonomy -- challenges that humans handle with ease. Humans excel at abstract reasoning, allowing them to perform complex tasks without constant attention to low-level details. This research project aims to equip robots with similar capabilities, enabling them to learn abstract representations of their environment and actions through experience. By enhancing the ability of robotic agents to plan and execute tasks in real world settings, this research could drive advancements in automation, assistive care, and disaster response. Additionally, the project intends to contribute to STEM education through outreach programs for high school students and research opportunities for undergraduate students. The findings will be disseminated through leading robotics conferences and peer-reviewed journals, ensuring broad visibility within the research community. Moreover, the results will inform and enhance robotics courses and all software and datasets produced will be openly shared, fostering collaboration and further advancements in the field. This project aims to advance robotic manipulation in unstructured environments by enabling robots to autonomously learn abstract representations of states and actions from sensory and execut