  Robots with multi-fingered grippers could perform advanced manipulation tasks
for us if we were able to properly specify to them what to do. In this study,
we take a step in that direction by making a robot grasp an object like a
grasping demonstration performed by a human. We propose a novel
optimization-based approach for transferring human grasp demonstrations to any
multi-fingered grippers, which produces robotic grasps that mimic the human
hand orientation and the contact area with the object, while alleviating
interpenetration. Extensive experiments with the Allegro and BarrettHand
grippers show that our method leads to grasps more similar to the human
demonstration than existing approaches, without requiring any gripper-specific
tuning. We confirm these findings through a user study and validate the
applicability of our approach on a real robot.
