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.

翻译：在现实场景中，物体在抓取前通常需要重新定位和定向，这一过程称为预抓取操控。学习通用的灵巧功能性预抓取操控，需要精确控制手与物体之间的相对位置、姿态和接触关系，同时泛化到包含不同物体和目标姿态的多样化动态场景中。为应对这一挑战，我们提出了一种师生学习方法，该方法利用一种新颖的互惠奖励机制，激励智能体联合优化三个关键准则。此外，我们引入了一个流程，该流程采用混合专家策略学习多样化操控策略，随后使用扩散策略从这些专家中捕获复杂动作分布。我们的方法通过利用外在灵巧性及反馈调整，在超过30种物体类别上实现了72.6%的成功率。