シナリオ拡張による深層強化学習ベースのロボットナビゲーションの汎化性能向上
Enhancing Deep Reinforcement Learning-based Robot Navigation Generalization through Scenario Augmentation
観測を想像空間に写像して行動を生成・再写像するシナリオ拡張を提案し、未見環境での深層強化学習ナビゲーションの汎化性能を大幅に改善した。
著者: Shanze Wang, Mingao Tan, Zhibo Yang, Xianghui Wang, Xiaoyu Shen, Hailong Huang, Wei Zhang
分類: cs.RO
原文アブストラクト
This work focuses on enhancing the generalization performance of deep reinforcement learning-based robot navigation in unseen environments. We present a novel data augmentation approach called scenario augmentation, which enables robots to navigate effectively across diverse settings without altering the training scenario. The method operates by mapping the robot's observation into an imagined space, generating an imagined action based on this transformed observation, and then remapping this action back to the real action executed in simulation. Through scenario augmentation, we conduct extensive comparative experiments to investigate the underlying causes of suboptimal navigation behaviors in unseen environments. Our analysis indicates that limited training scenarios represent the primary factor behind these undesired behaviors. Experimental results confirm that scenario augmentation substantially enhances the generalization capabilities of deep reinforcement learning-based navigation systems. The improved navigation framework demonstrates exceptional performance by producing near-optimal trajectories with significantly reduced navigation time in real-world applications.
