  While deep learning enables real robots to perform complex tasks had been
difficult to implement in the past, the challenge is the enormous amount of
trial-and-error and motion teaching in a real environment. The manipulation of
moving objects, due to their dynamic properties, requires learning a wide range
of factors such as the object's position, movement speed, and grasping timing.
We propose a data augmentation method for enabling a robot to grasp moving
objects with different speeds and grasping timings at low cost. Specifically,
the robot is taught to grasp an object moving at low speed using teleoperation,
and multiple data with different speeds and grasping timings are generated by
down-sampling and padding the robot sensor data in the time-series direction.
By learning multiple sensor data in a time series, the robot can generate
motions while adjusting the grasping timing for unlearned movement speeds and
sudden speed changes. We have shown using a real robot that this data
augmentation method facilitates learning the relationship between object
position and velocity and enables the robot to perform robust grasping motions
for unlearned positions and objects with dynamically changing positions and
velocities.
