注意機構付き深層強化学習による動的環境でのサブゴール駆動ナビゲーション
Subgoal-Driven Navigation in Dynamic Environments Using Attention-Based Deep Reinforcement Learning
2D LiDARに注意機構を適用し、衝突回避のための次サブゴール予測と速度制御を階層的に学習するナビゲーション手法を提案し、実機実験で成功率を向上させた。
著者: Jorge de Heuvel, Weixian Shi, Xiangyu Zeng, Maren Bennewitz
分類: cs.RO
原文アブストラクト
Collision-free, goal-directed navigation in environments containing unknown static and dynamic obstacles is still a great challenge, especially when manual tuning of navigation policies or costly motion prediction needs to be avoided. In this paper, we therefore propose a subgoal-driven hierarchical navigation architecture that is trained with deep reinforcement learning and decouples obstacle avoidance and motor control. In particular, we separate the navigation task into the prediction of the next subgoal position for avoiding collisions while moving toward the final target position, and the prediction of the robot's velocity controls. By relying on 2D lidar, our method learns to avoid obstacles while still achieving goal-directed behavior as well as to generate low-level velocity control commands to reach the subgoals. In our architecture, we apply the attention mechanism on the robot's 2D lidar readings and compute the importance of lidar scan segments for avoiding collisions. As we show in simulated and real-world experiments with a Turtlebot robot, our proposed method leads to smooth and safe trajectories among humans and significantly outperforms a state-of-the-art approach in terms of success rate. A supplemental video describing our approach is available online.
