Robotic grasping refers to making a robotic system pick an object by applying forces and torques on its surface. Many recent studies use data-driven approaches to address grasping, but the sparse reward nature of this task made the learning process challenging to bootstrap. To avoid constraining the operational space, an increasing number of works propose grasping datasets to learn from. But most of them are limited to simulations. The present paper investigates how automatically generated grasps can be exploited in the real world. More than 7000 reach-and-grasp trajectories have been generated with Quality-Diversity (QD) methods on 3 different arms and grippers, including parallel fingers and a dexterous hand, and tested in the real world. Conducted analysis on the collected measure shows correlations between several Domain Randomization-based quality criteria and sim-to-real transferability. Key challenges regarding the reality gap for grasping have been identified, stressing matters on which researchers on grasping should focus in the future. A QD approach has finally been proposed for making grasps more robust to domain randomization, resulting in a transfer ratio of 84% on the Franka Research 3 arm.

翻译：机器人抓取是指通过施加力和力矩使机器人系统拾取物体。近年来许多研究采用数据驱动方法解决抓取问题，但该任务的稀疏奖励特性使得学习过程难以启动。为避免约束操作空间，越来越多研究提出通过抓取数据集进行学习，但大多数局限于仿真环境。本文探讨如何将自动生成的抓取姿态应用于现实世界。通过质量-多样性方法在3种不同机械臂和夹爪（包括平行二指夹爪与灵巧手）上生成超过7000条"到达-抓取"轨迹，并在真实环境中进行测试。对采集数据的分析表明，多个基于域随机化的质量标准与仿真到现实迁移能力存在相关性。研究确定了抓取领域现实差距的关键挑战，指出了未来抓取研究者应重点关注的问题。最终提出一种基于质量-多样性方法使抓取动作对域随机化更具鲁棒性，在Franka Research 3机械臂上实现了84%的迁移成功率。