幻覚から学ぶ：狭所での俊敏な移動を実現する新ナビゲーション学習法
Toward Agile Maneuvers in Highly Constrained Spaces: Learning from Hallucination
安全な環境で集めたデータから、狭い空間でも俊敏かつ安全に動けるナビゲーション制御器を学習する「幻覚学習(LfH)」を提案し、実機で既存手法を上回る性能を示した。
著者: Xuesu Xiao, Bo Liu, Garrett Warnell, Peter Stone
分類: cs.RO
原文アブストラクト
While classical approaches to autonomous robot navigation currently enable operation in certain environments, they break down in tightly constrained spaces, e.g., where the robot needs to engage in agile maneuvers to squeeze between obstacles. Recent machine learning techniques have the potential to address this shortcoming, but existing approaches require vast amounts of navigation experience for training, during which the robot must operate in close proximity to obstacles and risk collision. In this paper, we propose to side-step this requirement by introducing a new machine learning paradigm for autonomous navigation called learning from hallucination (LfH), which can use training data collected in completely safe environments to compute navigation controllers that result in fast, smooth, and safe navigation in highly constrained environments. Our experimental results show that the proposed LfH system outperforms three autonomous navigation baselines on a real robot and generalizes well to unseen environments, including those based on both classical and machine learning techniques.
