  In real-world scenarios, objects often require repositioning and
reorientation before they can be grasped, a process known as pre-grasp
manipulation. Learning universal dexterous functional pre-grasp manipulation
requires precise control over the relative position, orientation, and contact
between the hand and object while generalizing to diverse dynamic scenarios
with varying objects and goal poses. To address this challenge, we propose a
teacher-student learning approach that utilizes a novel mutual reward,
incentivizing agents to optimize three key criteria jointly. Additionally, we
introduce a pipeline that employs a mixture-of-experts strategy to learn
diverse manipulation policies, followed by a diffusion policy to capture
complex action distributions from these experts. Our method achieves a success
rate of 72.6\% across more than 30 object categories by leveraging extrinsic
dexterity and adjusting from feedback.
