  Manipulating objects with robotic hands is a complicated task. Not only the
fingers of the hand, but also the pose of the robot's end effector need to be
coordinated. Using human demonstrations of movements is an intuitive and
data-efficient way of guiding the robot's behavior. We propose a modular
framework with an automatic embodiment mapping to transfer recorded human hand
motions to robotic systems. In this work, we use motion capture to record human
motion. We evaluate our approach on eight challenging tasks, in which a robotic
hand needs to grasp and manipulate either deformable or small and fragile
objects. We test a subset of trajectories in simulation and on a real robot and
the overall success rates are aligned.
