We introduce a large-scale dataset named MultiGripperGrasp for robotic grasping. Our dataset contains 30.4M grasps from 11 grippers for 345 objects. These grippers range from two-finger grippers to five-finger grippers, including a human hand. All grasps in the dataset are verified in Isaac Sim to classify them as successful and unsuccessful grasps. Additionally, the object fall-off time for each grasp is recorded as a grasp quality measurement. Furthermore, the grippers in our dataset are aligned according to the orientation and position of their palms, allowing us to transfer grasps from one gripper to another. The grasp transfer significantly increases the number of successful grasps for each gripper in the dataset. Our dataset is useful to study generalized grasp planning and grasp transfer across different grippers.

翻译：我们介绍一个名为MultiGripperGrasp的大规模机器人抓取数据集。该数据集包含来自11种夹持器对345个物体产生的3040万个抓取姿态。这些夹持器的范围涵盖两指夹爪到五指夹持器，并包括一只人类手部。数据集中所有抓取姿态均在Isaac Sim中经过验证，以区分成功与失败的抓取操作。此外，我们记录了每个抓取操作中物体掉落的时间，作为抓取质量的量化指标。同时，数据集中的夹持器根据其手掌的方向和位置进行对齐，使得抓取姿态可以在不同夹持器之间转移。这种抓取转移显著增加了数据集中每个夹持器的成功抓取数量。该数据集对于研究跨不同夹持器的通用抓取规划及抓取转移具有重要价值。