统一夹爪坐标系让不同夹爪能共享抓取策略，提升抓取泛化能力。
RobotFingerPrint: Unified Gripper Coordinate Space for Multi-Gripper Grasp Synthesis and Transfer
- 用球坐标建立跨夹爪的统一抓取空间，实现抓取点对点映射。
- 通过变分自编码器预测物体表面抓取坐标，生成稳定多样抓取姿态。
- 支持新物体和未见过夹爪的抓取合成与真实世界抓取迁移。
我们提出一种新型抓取表示方法——统一夹爪坐标空间（UGCS），用于抓取生成与抓取迁移。该方法利用球坐标构建不同机器人夹爪间的共享坐标空间，可对新物体及未见夹爪进行抓取生成与迁移。其核心在于将夹爪掌心与指节映射至统一坐标空间，抓取生成被建模为基于条件变分自编码器预测物体表面点的统一球坐标；所预测的统一夹爪坐标建立了夹爪与物体点之间的精确对应关系，用于优化抓取位姿与关节值。抓取迁移通过任意两夹爪（可能未见过）间的点对点对应关系实现，同样采用优化求解。大量仿真与真实实验验证了该统一抓取表示在生成稳定、多样化抓取方面的有效性，并展示了从人类示范到不同物体的真实世界抓取迁移能力。
原文摘要 · Abstract (English)
We introduce a novel grasp representation named the Unified Gripper Coordinate Space (UGCS) for grasp synthesis and grasp transfer. Our representation leverages spherical coordinates to create a shared coordinate space across different robot grippers, enabling it to synthesize and transfer grasps for both novel objects and previously unseen grippers. The strength of this representation lies in the ability to map palm and fingers of a gripper and the unified coordinate space. Grasp synthesis is formulated as predicting the unified spherical coordinates on object surface points via a conditional variational autoencoder. The predicted unified gripper coordinates establish exact correspondences between the gripper and object points, which is used to optimize grasp pose and joint values. Grasp transfer is facilitated through the point-to-point correspondence between any two (potentially unseen) grippers and solved via a similar optimization. Extensive simulation and real-world experiments showcase the efficacy of the unified grasp representation for grasp synthesis in generating stable and diverse grasps. Similarly, we showcase real-world grasp transfer from human demonstrations across different objects.
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