オフロード走行のための高速インクリメンタル学習
Fast Incremental Learning for Off-Road Robot Navigation
ImageNetで事前学習したモデルを土台に、対象環境の少量データで素早く適応させる自律走行ナビゲーション手法を提案した論文。
著者: Artem Provodin, Liila Torabi, Beat Flepp, Yann LeCun, Michael Sergio, L. D. Jackel, Urs Muller, Jure Zbontar
分類: cs.RO
原文アブストラクト
A promising approach to autonomous driving is machine learning. In such systems, training datasets are created that capture the sensory input to a vehicle as well as the desired response. A disadvantage of using a learned navigation system is that the learning process itself may require a huge number of training examples and a large amount of computing. To avoid the need to collect a large training set of driving examples, we describe a system that takes advantage of the huge number of training examples provided by ImageNet, but is able to adapt quickly using a small training set for the specific driving environment.
