Abstract-We formalize the problem of adapting a demonstrated trajectory to a new start and goal configuration as an optimization problem over a Hilbert space of trajectories: minimize the distance between the demonstration and the new trajectory subject to the new end point constraints. We show that the commonly used version of Dynamic Movement Primitives (DMPs) implement this minimization in the way they adapt demonstrations, for a particular choice of the Hilbert space norm. The generalization to arbitrary norms enables the robot to select a more appropriate norm for the task, as well as learn how to adapt the demonstration from the user. Our experiments show that this can significantly improve the robot's ability to accurately generalize the demonstration.
An optimization framework proposed in REF attempts to adapt multiple demonstrations to new initial and target locations by minimizing the distance between the demonstrations and the reproduction according to a learned Hilbert space norm.
Movement primitives via optimization
2015 IEEE International Conference on Robotics and Automation (ICRA)
2015 IEEE International Conference on Robotics and Automation (ICRA)
Mathematics Computer Science