The use of artificial intelligence in cyber-physical systems is limited by challenges such as data availability, task environment complexity, and the need for expressive and interpretable high-level knowledge representations. To address these challenges, this project aims to develop a set of neuro-symbolic learning and control tools by integrating machine learning, control theory, and formal methods. The results are expected to find application across cyber-physical systems such as robotic systems, autonomous systems, and networked cyber-physical systems. Validation in a testbed environments should facilitate safe deployments in real-world physical environments with provable guarantees and robustness against potential adversaries.