The success of deep learning depends heavily on the availability of large datasets, but in robotic manipulation there are many learning problems for which such datasets do not exist. Collecting these datasets is time-consuming and expensive, and therefore learning from small datasets is an important open problem. Within computer vision, a common approach to a lack of data is data augmentation. Data augmentation is the process of creating additional training examples by modifying existing ones. However, because the types of tasks and data differ, the methods used in computer vision cannot be easily adapted to manipulation. Therefore, we propose a data augmentation method for robotic manipulation. We argue that augmentations should be valid, relevant, and diverse. We use these principles to formalize augmentation as an optimization problem, with the objective function derived from physics and knowledge of the manipulation domain. This method applies rigid body transformations to trajectories of geometric state and action data. We test our method in two scenarios: 1) learning the dynamics of planar pushing of rigid cylinders, and 2) learning a constraint checker for rope manipulation. These two scenarios have different data and label types, yet in both scenarios, training on our augmented data significantly improves performance on downstream tasks. We also show how our augmentation method can be used on real-robot data to enable more data-efficient online learning.
翻译:深层次学习的成功在很大程度上取决于大型数据集的可用性,但在机器人操作中,有许多没有这类数据集的学习问题。收集这些数据集耗时费钱,因此从小数据集中学习是一个重要的开放问题。在计算机的愿景中,对缺乏数据的一种共同做法是数据增强。数据扩增是通过修改现有数据来创建更多培训范例的过程。然而,由于任务和数据的类型不同,计算机视觉中使用的方法无法轻易地适应操作。因此,我们建议了机器人操作的数据增强方法。我们认为,增强值应该是有效、相关和多样的。我们用这些原则正式确定增强是一个优化问题,其目标功能来自物理和操作领域的知识。这种方法将僵硬体转换应用于几何状态和行动数据的轨迹。我们用两种假设来测试我们的方法:(1) 学习平板推动硬质圆筒的动态,(2) 学习绳索操作的制约校验器。这两种假设有不同的数据和标签类型,但在两种假设中,我们使用这些原则来将增强数据配置为优化的优化性,同时,我们用这种方法来大大改进了我们数据升级的在线数据学习方法。