The ability to use tools is one of the hallmarks of intelligence. Tool use is fundamental to human life and has been for at least the last two million years. We use tools to extend our reach, to amplify our physical strength, and to achieve many other tasks. A large number of animals have also been observed to use tools. Despite the widespread use of tools in the animal world, however, studies of autonomous robotic tool use are still rare. This dissertation examines the problem of autonomous tool use in robots from the point of view of developmental robotics. Therefore, the main focus is not on optimizing robotic solutions for specific tool tasks but on designing algorithms and representations that a robot can use to develop tool-using abilities. The dissertation describes a developmental sequence/trajectory that a robot can take in order to learn how to use tools autonomously. The developmental sequence begins with learning a model of the robot's body since the body is the most consistent and predictable part of the environment. Specifically, the robot learns which perceptual features are associated with its own body and which with the environment. Next, the robot can begin to identify certain patterns exhibited by the body itself and to learn a robot body schema model which can also be used to encode goal-oriented behaviors. The robot can also use its body as a well defined reference frame from which the properties of environmental objects can be explored by relating them to the body. Finally, the robot can begin to relate two environmental objects to one another and to learn that certain actions with the first object can affect the second object, i.e., the first object can be used as a tool. The main contributions of the dissertation can be broadly summarized as follows: it demonstrates a method for autonomous self-detection in robots; it demonstrates a model for extendable robot body schema which can be used to achieve goal-oriented behaviors, including video-guided behaviors; it demonstrates a behavior-grounded method for learning the affordances of tools which can also be used to solve tool-using tasks.