Learning-from-Demonstrations

The paper proposes a method using motion and tactile features to automatically segment atomic actions from teleoperated demonstrations for complex robotic tasks, provides a dataset of pick-and-place teleoperation with a dexterous hand, and shows the proposed features generalize between episodes and similar objects while tactile sensing improves activity recognition within demonstrations.