  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.

Robotics