DDGC：杂乱环境中的生成式深度灵巧抓取
DDGC: Generative Deep Dexterous Grasping in Clutter
AI总结：
本文提出DDGC生成式多指抓取采样网络，从单张RGB-D图像由粗到细生成杂乱场景中的无碰撞抓取，实验表明其质量优于GraspIt!基线且速度提升5倍。
摘要：
多指机器人抓取的最新进展已实现快速的6自由度（DOF）单物体抓取。然而，杂乱场景中的多指抓取在很大程度上仍未得到充分探索，因为需要对障碍物进行推理，这显著增加了生成高质量无碰撞抓取所需的计算时间。在本工作中，我们通过提出DDGC来解决这些局限，它是一种快速的生成式多指抓取采样方法，能够仅根据单张RGB-D图像在杂乱场景中生成高质量抓取。DDGC被构建为一个网络，对场景信息进行编码，以生成由粗到细的无碰撞抓取位姿和构型。我们在1200个仿真杂乱场景和7个真实世界场景上，将DDGC与GraspIt!中的模拟退火规划器进行了实验基准比较。结果表明，DDGC在合成高质量抓取和清除杂物方面优于基线，同时速度快5倍。这进而为在实际应用中使用多指抓取打开了大门；迄今为止，由于其他方法需要过多计算时间，这类应用一直受到限制。
英文摘要：
Recent advances in multi-fingered robotic grasping have enabled fast 6-Degrees-Of-Freedom (DOF) single object grasping. Multi-finger grasping in cluttered scenes, on the other hand, remains mostly unexplored due to the added difficulty of reasoning over obstacles which greatly increases the computational time to generate high-quality collision-free grasps. In this work we address such limitations by introducing DDGC, a fast generative multi-finger grasp sampling method that can generate high quality grasps in cluttered scenes from a single RGB-D image. DDGC is built as a network that encodes scene information to produce coarse-to-fine collision-free grasp poses and configurations. We experimentally benchmark DDGC against the simulated-annealing planner in GraspIt! on 1200 simulated cluttered scenes and 7 real world scenes. The results show that DDGC outperforms the baseline on synthesizing high-quality grasps and removing clutter while being 5 times faster. This, in turn, opens up the door for using multi-finger grasps in practical applications which has so far been limited due to the excessive computation time needed by other methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
