ExAug: Robot-Conditioned Navigation Policies via Geometric Experience Augmentation

Understand

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.