社会的配慮を伴うロボットナビゲーションのための世界遷移モデル学習
Learning World Transition Model for Socially Aware Robot Navigation
レーザスキャン列から自己運動を分離した局所障害物マップを作り、深い遷移モデルで周囲の動態を予測しながら強化学習で混雑環境のナビゲーション方策を学習した研究。
著者: Yuxiang Cui, Haodong Zhang, Yue Wang, Rong Xiong
分類: cs.RO
原文アブストラクト
Moving in dynamic pedestrian environments is one of the important requirements for autonomous mobile robots. We present a model-based reinforcement learning approach for robots to navigate through crowded environments. The navigation policy is trained with both real interaction data from multi-agent simulation and virtual data from a deep transition model that predicts the evolution of surrounding dynamics of mobile robots. The model takes laser scan sequence and robot's own state as input and outputs steering control. The laser sequence is further transformed into stacked local obstacle maps disentangled from robot's ego motion to separate the static and dynamic obstacles, simplifying the model training. We observe that our method can be trained with significantly less real interaction data in simulator but achieve similar level of success rate in social navigation task compared with other methods. Experiments were conducted in multiple social scenarios both in simulation and on real robots, the learned policy can guide the robots to the final targets successfully while avoiding pedestrians in a socially compliant manner. Code is available at https://github.com/YuxiangCui/model-based-social-navigation
