  Re-grasp manipulation leverages on ergonomic tools to assist humans in
accomplishing diverse tasks. In certain scenarios, humans often employ external
forces to effortlessly and precisely re-grasp tools like a hammer. Previous
development on controllers for in-grasp sliding motion using passive dynamic
actions (e.g.,gravity) relies on apprehension of finger-object contact
information, and requires customized design for individual objects with varied
geometry and weight distribution. It limits their adaptability to diverse
objects. In this paper, we propose an end-to-end sliding motion controller
based on imitation learning (IL) that necessitates minimal prior knowledge of
object mechanics, relying solely on object position information. To expedite
training convergence, we utilize a data glove to collect expert data
trajectories and train the policy through Generative Adversarial Imitation
Learning (GAIL). Simulation results demonstrate the controller's versatility in
performing in-hand sliding tasks with objects of varying friction coefficients,
geometric shapes, and masses. By migrating to a physical system using visual
position estimation, the controller demonstrated an average success rate of
86%, surpassing the baseline algorithm's success rate of 35% of Behavior
Cloning(BC) and 20% of Proximal Policy Optimization (PPO).
