论文标题
用于机器人人群导航的分散结构RNN，并深入强化学习
Decentralized Structural-RNN for Robot Crowd Navigation with Deep Reinforcement Learning
论文作者
论文摘要
通过人类人群的安全有效航行是移动机器人的重要能力。机器人人群导航的先前工作假设所有代理的动力学都是已知且定义明确的。此外，先前方法的性能在部分可观察到的环境和密集人群的环境中恶化。为了解决这些问题，我们提出了一个分散的结构性神经网络（DS-RNN），这是一个新颖的网络，原因是关于人群导航中机器人决策的空间和时间关系的原因。我们在没有任何专家监督的情况下，通过无模型的深度强化学习来培训我们的网络。我们证明，在挑战人群导航方案中，我们的模型优于以前的方法。我们成功地将模拟器中学到的政策转移到了现实世界中的Turtlebot 2i。有关更多信息，请访问项目网站https://sites.google.com/view/crowdnav-dnav-ds-rnn/home。
Safe and efficient navigation through human crowds is an essential capability for mobile robots. Previous work on robot crowd navigation assumes that the dynamics of all agents are known and well-defined. In addition, the performance of previous methods deteriorates in partially observable environments and environments with dense crowds. To tackle these problems, we propose decentralized structural-Recurrent Neural Network (DS-RNN), a novel network that reasons about spatial and temporal relationships for robot decision making in crowd navigation. We train our network with model-free deep reinforcement learning without any expert supervision. We demonstrate that our model outperforms previous methods in challenging crowd navigation scenarios. We successfully transfer the policy learned in the simulator to a real-world TurtleBot 2i. For more information, please visit the project website at https://sites.google.com/view/crowdnav-ds-rnn/home.
