  Dexterous manipulation through imitation learning has gained significant
attention in robotics research. The collection of high-quality expert data
holds paramount importance when using imitation learning. The existing
approaches for acquiring expert data commonly involve utilizing a data glove to
capture hand motion information. However, this method suffers from limitations
as the collected information cannot be directly mapped to the robotic hand due
to discrepancies in their degrees of freedom or structures. Furthermore,it
fails to accurately capture force feedback information between the hand and
objects during the demonstration process. To overcome these challenges, this
paper presents a novel solution in the form of a wearable dexterous hand,
namely Hand-over-hand Imitation learning wearable RObotic Hand (HIRO
Hand),which integrates expert data collection and enables the implementation of
dexterous operations. This HIRO Hand empowers the operator to utilize their own
tactile feedback to determine appropriate force, position, and actions,
resulting in more accurate imitation of the expert's actions. We develop both
non-learning and visual behavior cloning based controllers allowing HIRO Hand
successfully achieves grasping and in-hand manipulation ability.
