DexUMI: Using Human Hand as the Universal Manipulation Interface for
  Dexterous Manipulation
Authors: name: Mengda Xu, name: Han Zhang, name: Yifan Hou, name: Zhenjia Xu, name: Linxi Fan, name: Manuela Veloso, name: Shuran Song

We present DexUMI - a data collection and policy learning framework that uses
the human hand as the natural interface to transfer dexterous manipulation
skills to various robot hands. DexUMI includes hardware and software
adaptations to minimize the embodiment gap between the human hand and various
robot hands. The hardware adaptation bridges the kinematics gap using a
wearable hand exoskeleton. It allows direct haptic feedback in manipulation
data collection and adapts human motion to feasible robot hand motion. The
software adaptation bridges the visual gap by replacing the human hand in video
data with high-fidelity robot hand inpainting. We demonstrate DexUMI's
capabilities through comprehensive real-world experiments on two different
dexterous robot hand hardware platforms, achieving an average task success rate
of 86%.