论文标题
学习世界过渡模型的社会意识到机器人导航
Learning World Transition Model for Socially Aware Robot Navigation
论文作者
论文摘要
在动态的行人环境中移动是自动移动机器人的重要要求之一。我们提出了一种基于模型的增强学习方法，以供机器人在拥挤的环境中导航。从多代理模拟的实际交互数据和深度过渡模型的虚拟数据培训了导航策略，该数据可预测移动机器人的周围动力学的演变。该模型将激光扫描序列和机器人自己的状态作为输入和输出转向控制。激光序列进一步转化为从机器人的自我运动中分离的堆叠局部障碍图，以分离静态和动态障碍，从而简化了模型训练。我们观察到，我们的方法可以接受模拟器中的实际交互数据的培训，但与其他方法相比，社会导航任务的成功率相似。在模拟和真实机器人的多种社交场景中进行了实验，学识渊博的政策可以成功地指导机器人到最终目标，同时以社会规定的方式避开行人。代码可从https://github.com/yuxiangcui/model基于基础 - 苏联nevigation获得
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
