Dynamical models of robots performing tasks in contact with objects or the environment are difficult to obtain. Therefore, different methods of learning the dynamics of tasks have been proposed. In this chapter, we present a method that provides the joint torques needed to execute a task in a compliant and at the same time accurate manner. The presented method of compliant movement primitives (CMPs), which consists of the task kinematical and dynamical trajectories, goes beyond mere reproduction of previously learned motions. Using statistical generalization, the method allows to generate new, previously untrained trajectories. Furthermore, the use of transition graphs allows us to combine parts of previously learned motions and thus generate new ones. In the chapter, we provide a brief overview of this research topic in the literature, followed by an in-depth explanation of the compliant movement primitives framework, with details on both statistical generalization and transition graphs. An extensive experimental evaluation demonstrates the applicability and the usefulness of the approach.
Part of the book: Robot Control