ExAug: 幾何学的経験拡張によるロボット条件付きナビゲーションポリシー
ExAug: Robot-Conditioned Navigation Policies via Geometric Experience Augmentation
異なるロボットのデータセットを点群ベースの幾何学的拡張で統合し、新しい機体でも汎用化できるナビゲーションポリシーを学習するフレームワークを提案。
著者: Noriaki Hirose, Dhruv Shah, Ajay Sridhar, Sergey Levine
分類: cs.RO, cs.AI, cs.CV, cs.LG
原文アブストラクト
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.
