Machine learning techniques rely on large and diverse datasets for
generalization. Computer vision, natural language processing, and other
applications can often reuse public datasets to train many different models.
However, due to differences in physical configurations, it is challenging to
leverage public datasets for training robotic control policies on new robot
platforms or for new tasks. In this work, we propose a novel framework, ExAug
to augment the experiences of different robot platforms from multiple datasets
in diverse environments. ExAug leverages a simple principle: by extracting 3D
information in the form of a point cloud, we can create much more complex and
structured augmentations, utilizing both generating synthetic images and
geometric-aware penalization that would have been suitable in the same
situation for a different robot, with different size, turning radius, and
camera placement. The trained policy is evaluated on two new robot platforms
with three different cameras in indoor and outdoor environments with obstacles.

本研究提出了一种新颖的框架 ExAug，通过利用点云来创造更复杂的结构增强，并利用机器学习中的生成图像和几何惩罚来帮助不同机器人平台进行新任务的训练，实现在不同环境中对新机器人平台的控制策略训练。