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.

本研究探讨了如何利用自动生成的抓取方法来解决机器人在实际环境中获取对象的问题，并就域随机化、从仿真到实际的转移性等问题进行了分析和研究。

自动生成抓取数据集的域随机化用于模拟到真实场景迁移

Domain Randomization for Sim2real Transfer of Automatically Generated  Grasping Datasets

Robotic grasping is still a partially solved, multidisciplinary problem where
data-driven techniques play an increasing role. The sparse nature of rewards
make the automatic generation of grasping datasets challenging, especially for
unconventional morphologies or highly actuated end-effectors. Most approaches
for obtaining large-scale datasets rely on numerous human-provided
demonstrations or heavily engineered solutions that do not scale well. Recent
advances in Quality-Diversity (QD) methods have investigated how to learn
object grasping at a specific pose with different robot morphologies. The
present work introduces a pipeline for adapting QD-generated trajectories to
new object poses. Using an RGB-D data stream, the vision pipeline first detects
the targeted object, predicts its 6-DOF pose, and finally tracks it. An
automatically generated reach-and-grasp trajectory can then be adapted by
projecting it relatively to the object frame. Hundreds of trajectories have
been deployed into the real world on several objects and with different robotic
setups: a Franka Research 3 with a parallel gripper and a UR5 with a dexterous
SIH Schunk hand. The transfer ratio obtained when applying transformation to
the object pose matches the one obtained when the object pose matches the
simulation, demonstrating the efficiency of the proposed approach.

通过使用 RGB-D 数据流和视觉技术，本研究提出了一种自动生成并适应新物体姿态的抓取轨迹的方法，通过将轨迹相对于物体框架进行投影，实现了在多个不同机器人设置和物体上的抓取任务的高效执行。