Learning interaction control policies by demonstration

This paper explores learning of interaction force skills by human demonstration in dynamic interaction tasks. Skillful force regulation is required in many cases to achieve the goal of a task and at the same time, not to cause undesired stress on the manipulator or the object under manipulation which could result in physical failure. For example, manipulation of compliant objects with varying physical properties or artistic tasks such as engraving require skillful force modulation. Humans gracefully manipulate objects by using their sense of touch and skillfully regulating exerted forces. To learn the demonstrated force for a task by demonstration, an interaction force control policy, in terms of a goal-directed dynamical system, is proposed which stems from the parallel force/position control. The control policy is parameterized and its parameters are learned by Locally Weighted Regression from human demonstrated data to learn a force trajectory. Scaling of learned force is possible by modifying the goal of the system. The proposed method is evaluated in virtual manipulation tasks using a two degrees-of-freedom haptic device.