ExAug: Robot-Conditioned Navigation Policies via Geometric Experience Augmentation
This addresses the problem of dataset reuse for robotic control, enabling generalization across different physical configurations, though it appears incremental as it builds on existing augmentation techniques.
The paper tackles the challenge of leveraging public datasets for training robotic control policies on new robot platforms or tasks by proposing ExAug, a framework that augments experiences using 3D point clouds to generate synthetic images and geometric-aware penalization, resulting in successful evaluation on two new robot platforms with three cameras in indoor and outdoor environments.
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.