Using Simulation and Domain Adaptation to Improve Efficiency of Deep Robotic Grasping
This work addresses the high cost of data collection for robotic grasping, offering a practical solution for robotics researchers and engineers, though it is incremental in combining existing simulation and domain adaptation techniques.
The paper tackles the problem of training deep robotic grasping models without expensive real-world data by using synthetic data from simulation and domain adaptation, achieving a 50x reduction in real-world samples needed for comparable performance and matching the performance of nearly 940,000 labeled real-world samples using only unlabeled data.
Instrumenting and collecting annotated visual grasping datasets to train modern machine learning algorithms can be extremely time-consuming and expensive. An appealing alternative is to use off-the-shelf simulators to render synthetic data for which ground-truth annotations are generated automatically. Unfortunately, models trained purely on simulated data often fail to generalize to the real world. We study how randomized simulated environments and domain adaptation methods can be extended to train a grasping system to grasp novel objects from raw monocular RGB images. We extensively evaluate our approaches with a total of more than 25,000 physical test grasps, studying a range of simulation conditions and domain adaptation methods, including a novel extension of pixel-level domain adaptation that we term the GraspGAN. We show that, by using synthetic data and domain adaptation, we are able to reduce the number of real-world samples needed to achieve a given level of performance by up to 50 times, using only randomly generated simulated objects. We also show that by using only unlabeled real-world data and our GraspGAN methodology, we obtain real-world grasping performance without any real-world labels that is similar to that achieved with 939,777 labeled real-world samples.