This project aims to enhance complex, robust, and general robot manipulation learning through inductive biases based on structured regularities in the perception/action space. The biases will be hierarchical and composed of regularities at different levels of abstraction. They will be validated in a contact-rich manipulation task using a highly capable hand/arm system with multi-modal sensors, resulting in a powerful and data-efficient learning approach.