リーマン運動ポリシーを用いたニューラル自律ナビゲーション
Neural Autonomous Navigation with Riemannian Motion Policy
画像から車両の制御点RMPを予測し解析的に最適制御指令を計算する深層学習ナビゲーション手法を提案し、実車での障害物回避と未知環境への汎化性能を実証した。
著者: Xiangyun Meng, Nathan Ratliff, Yu Xiang, Dieter Fox
分類: cs.RO
原文アブストラクト
End-to-end learning for autonomous navigation has received substantial attention recently as a promising method for reducing modeling error. However, its data complexity, especially around generalization to unseen environments, is high. We introduce a novel image-based autonomous navigation technique that leverages in policy structure using the Riemannian Motion Policy (RMP) framework for deep learning of vehicular control. We design a deep neural network to predict control point RMPs of the vehicle from visual images, from which the optimal control commands can be computed analytically. We show that our network trained in the Gibson environment can be used for indoor obstacle avoidance and navigation on a real RC car, and our RMP representation generalizes better to unseen environments than predicting local geometry or predicting control commands directly.
