基于离线预训练强化学习的避障导航
Avoidance Navigation Based on Offline Pre-Training Reinforcement Learning
AI总结：
本文提出一种基于离线预训练深度强化学习的无地图移动机器人避障导航方法，利用专家经验数据集和优先经验策略，显著减少训练时间并提升奖励，实现了跨场景的无碰撞泛化导航。
摘要：
本文提出了一种用于移动机器人无地图避障导航的预训练深度强化学习（DRL）方法，该方法将原始传感器数据映射到控制变量并在未知环境中进行导航。我们提出了高效的离线训练策略以加速早期低效的随机探索，并收集了一个包含专家经验的通用数据集用于离线训练，这对其他导航训练工作具有一定意义。通过预训练和优先专家经验，训练时间减少了80%，并经验证使DRL的奖励提高了2倍。集成了真实物理建模和动力学方程的高级仿真平台Gazebo缩小了从仿真到现实的差距。我们在走廊环境中训练模型，并在不同环境中评估该模型，取得了相同的效果。与传统导航方法相比，我们可以确认训练好的模型能够直接应用于不同场景，并具备无碰撞导航的能力。实验表明，我们的DRL模型在不同环境中具有普遍的泛化能力。
英文摘要：
This paper presents a Pre-Training Deep Reinforcement Learning(DRL) for avoidance navigation without map for mobile robots which map raw sensor data to control variable and navigate in an unknown environment. The efficient offline training strategy is proposed to speed up the inefficient random explorations in early stage and we also collect a universal dataset including expert experience for offline training, which is of some significance for other navigation training work. The pre-training and prioritized expert experience are proposed to reduce 80\% training time and has been verified to improve the 2 times reward of DRL. The advanced simulation gazebo with real physical modelling and dynamic equations reduce the gap between sim-to-real. We train our model a corridor environment, and evaluate the model in different environment getting the same effect. Compared to traditional method navigation, we can confirm the trained model can be directly applied into different scenarios and have the ability to no collision navigate. It was demonstrated that our DRL model have universal general capacity in different environment.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
