Towards Precise Model-free Robotic Grasping with Sim-to-Real Transfer Learning
This work addresses the problem of reliable robotic grasping for manufacturing and logistics, but it is incremental as it builds on existing sim-to-real and data augmentation techniques.
The study tackled the challenge of precise model-free robotic grasping by combining data generation and sim-to-real transfer learning, achieving success rates of 90.91% for single objects and 85.71% for multi-object scenarios, outperforming state-of-the-art methods.
Precise robotic grasping of several novel objects is a huge challenge in manufacturing, automation, and logistics. Most of the current methods for model-free grasping are disadvantaged by the sparse data in grasping datasets and by errors in sensor data and contact models. This study combines data generation and sim-to-real transfer learning in a grasping framework that reduces the sim-to-real gap and enables precise and reliable model-free grasping. A large-scale robotic grasping dataset with dense grasp labels is generated using domain randomization methods and a novel data augmentation method for deep learning-based robotic grasping to solve data sparse problem. We present an end-to-end robotic grasping network with a grasp optimizer. The grasp policies are trained with sim-to-real transfer learning. The presented results suggest that our grasping framework reduces the uncertainties in grasping datasets, sensor data, and contact models. In physical robotic experiments, our grasping framework grasped single known objects and novel complex-shaped household objects with a success rate of 90.91%. In a complex scenario with multi-objects robotic grasping, the success rate was 85.71%. The proposed grasping framework outperformed two state-of-the-art methods in both known and unknown object robotic grasping.