視覚メモリを用いたロボットの興味度推定のための教師なしオンライン学習
Unsupervised Online Learning for Robotic Interestingness with Visual Memory
翻訳不変な視覚メモリと長期・短期・オンライン学習の3段階構成により、訓練データなしで環境にオンライン適応し興味深いシーンを高速に検出する手法を提案した。
著者: Chen Wang, Yuheng Qiu, Wenshan Wang, Yafei Hu, Seungchan Kim, Sebastian Scherer
分類: cs.RO, cs.CV
原文アブストラクト
Autonomous robots frequently need to detect "interesting" scenes to decide on further exploration, or to decide which data to share for cooperation. These scenarios often require fast deployment with little or no training data. Prior work considers "interestingness" based on data from the same distribution. Instead, we propose to develop a method that automatically adapts online to the environment to report interesting scenes quickly. To address this problem, we develop a novel translation-invariant visual memory and design a three-stage architecture for long-term, short-term, and online learning, which enables the system to learn human-like experience, environmental knowledge, and online adaption, respectively. With this system, we achieve an average of 20% higher accuracy than the state-of-the-art unsupervised methods in a subterranean tunnel environment. We show comparable performance to supervised methods for robot exploration scenarios showing the efficacy of our approach. We expect that the presented method will play an important role in the robotic interestingness recognition exploration tasks.
