  We develop a method for learning periodic tasks from visual demonstrations.
The core idea is to leverage periodicity in the policy structure to model
periodic aspects of the tasks. We use active learning to optimize parameters of
rhythmic dynamic movement primitives (rDMPs) and propose an objective to
maximize the similarity between the motion of objects manipulated by the robot
and the desired motion in human video demonstrations. We consider tasks with
deformable objects and granular matter whose states are challenging to
represent and track: wiping surfaces with a cloth, winding cables/wires, and
stirring granular matter with a spoon. Our method does not require tracking
markers or manual annotations. The initial training data consists of 10-minute
videos of random unpaired interactions with objects by the robot and human. We
use these for unsupervised learning of a keypoint model to get task-agnostic
visual correspondences. Then, we use Bayesian optimization to optimize rDMPs
from a single human video demonstration within few robot trials. We present
simulation and hardware experiments to validate our approach.
