Domain Randomization for Sim2real Transfer of Automatically Generated Grasping Datasets
Johann Huber, Fran\c{c}ois H\'el\'enon, Hippolyte Watrelot, Faiz Ben, Amar, St\'ephane Doncieux

TL;DR
This paper explores how automatically generated grasping datasets using domain randomization can improve sim-to-real transfer in robotic grasping, achieving high transfer ratios and identifying key challenges in bridging the reality gap.
Contribution
It introduces a QD-based method to enhance grasp robustness under domain randomization, with extensive real-world testing across multiple robotic arms.
Findings
Over 7000 grasp trajectories generated and tested in real world
Correlation identified between quality criteria and transferability
Achieved 84% transfer ratio on Franka Research 3 arm
Abstract
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…
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Taxonomy
TopicsRobot Manipulation and Learning · Reinforcement Learning in Robotics · Machine Learning and Data Classification
MethodsFocus
