深層ネットワークを成長させながらナビゲーションを学習する
Learning to Navigate by Growing Deep Networks
単眼カメラ画像から自己教師ありでロボットのナビゲーションを学習し、強化学習でニューロンを追加・削除してネットワーク構造を動的に成長させるRA-DAEを提案した論文。
著者: Thushan Ganegedara, Lionel Ott, Fabio Ramos
分類: cs.RO, cs.CV
原文アブストラクト
Adaptability is central to autonomy. Intuitively, for high-dimensional learning problems such as navigating based on vision, internal models with higher complexity allow to accurately encode the information available. However, most learning methods rely on models with a fixed structure and complexity. In this paper, we present a self-supervised framework for robots to learn to navigate, without any prior knowledge of the environment, by incrementally building the structure of a deep network as new data becomes available. Our framework captures images from a monocular camera and self labels the images to continuously train and predict actions from a computationally efficient adaptive deep architecture based on Autoencoders (AE), in a self-supervised fashion. The deep architecture, named Reinforced Adaptive Denoising Autoencoders (RA-DAE), uses reinforcement learning to dynamically change the network structure by adding or removing neurons. Experiments were conducted in simulation and real-world indoor and outdoor environments to assess the potential of self-supervised navigation. RA-DAE demonstrates better performance than equivalent non-adaptive deep learning alternatives and can continue to expand its knowledge, trading-off past and present information.
