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While deep learning enables real robots to perform complex tasks had beendifficult to implement in the past, the challenge is the enormous amount oftrial-and-error and motion teaching in a real environment. The manipulation ofmoving objects, due to their dynamic properties, requires learning a wide rangeof factors such as the object's position, movement speed, and grasping timing.We propose a data augmentation method for enabling a robot to grasp movingobjects 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 bydown-sampling and padding the robot sensor data in the time-series direction.By learning multiple sensor data in a time series, the robot can generatemotions while adjusting the grasping timing for unlearned movement speeds andsudden speed changes. We have shown using a real robot that this dataaugmentation method facilitates learning the relationship between objectposition and velocity and enables the robot to perform robust grasping motionsfor unlearned positions and objects with dynamically changing positions andvelocities.

Real-time Motion Generation and Data Augmentation for Grasping Moving Objects with Dynamic Speed and Position Changes