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.

翻译：尽管深度学习使真实机器人能够执行复杂任务，但过去实现这一目标仍面临巨大挑战，其难点在于真实环境中需要海量的试错与运动示教。由于运动物体的动态特性，对其操纵需要学习物体位置、运动速度及抓取时机等广泛因素。我们提出一种数据增强方法，使机器人能够以低成本抓取不同速度与抓取时机的运动物体。具体而言，通过遥操作示教机器人抓取低速运动物体，并对机器人传感器数据沿时间序列方向进行降采样与填充，生成包含不同速度与抓取时机的多组数据。通过学习多组时间序列传感器数据，机器人能够针对未学习的运动速度及突发速度变化调整抓取时机并生成运动。我们通过真实机器人实验验证，该数据增强方法有助于学习物体位置与速度之间的关联，使机器人能够对未学习位置以及位置与速度动态变化的物体执行鲁棒性抓取运动。