This dissertation investigates two complementary ideas in the literature on machine learning and robotics--those of embodiment and intrinsic motivation--to address a unified framework for skill learning and knowledge acquisition. "Embodied" systems make use of structure derived directly from sensory and motor configurations for learning behavior. Intrinsically motivated systems learn by searching for native, hedonic value through interaction with the world. Psychological theories of intrinsic motivation suggest that there exist internal drives favoring open-ended cognitive development and exploration. I argue that intrinsically motivated, embodied systems can learn generalizable skills, acquire control knowledge, and form an epistemological understanding of the world in terms of behavioral affordances. I propose that the development of behavior results from the assembly of an agent's sensory and motor resources into state and action spaces that can be explored autonomously. I introduce an intrinsic reward function that can lead to the open-ended learning of hierarchical behavior. This behavior is factored into declarative "recipes" for patterned activity and common sense procedural strategies for implementing them in a variety of run-time contexts. These skills form a categorical basis for the robot to interpret and model its world in terms of the behavior it accords. Experiments conducted on a bimanual robot illustrate a progression of cumulative manipulation behavior addressing manual and visual skills. Such accumulation of skill over the long-term by a single robot is a novel contribution that has yet to be demonstrated in the literature.