结构化图网络用于低保真仿真下的约束机器人人群导航
Structured Graph Network for Constrained Robot Crowd Navigation with Low Fidelity Simulation
AI总结：
提出结构化图网络与注意力机制，利用低保真仿真分离人类与障碍物表示，减少sim2real差距，提升约束人群导航性能。
摘要：
我们研究了使用低保真模拟器部署强化学习（RL）策略进行约束人群导航的可行性。我们引入了一种动态环境的表示方法，将人类和障碍物的表示分开。人类通过检测到的状态来表示，而障碍物则通过基于地图和机器人定位计算的点云来表示。这种表示使得在低保真模拟器中训练的RL策略能够以更小的模拟到现实差距部署到现实世界中。此外，我们提出了一种时空图来建模智能体与障碍物之间的交互。基于该图，我们使用注意力机制来捕捉机器人-人类、人类-人类以及人类-障碍物的交互。我们的方法在模拟和现实环境中均显著提高了导航性能。视频演示可在 https://sites.google.com/view/constrained-crowdnav/home 查看。
英文摘要：
We investigate the feasibility of deploying reinforcement learning (RL) policies for constrained crowd navigation using a low-fidelity simulator. We introduce a representation of the dynamic environment, separating human and obstacle representations. Humans are represented through detected states, while obstacles are represented as computed point clouds based on maps and robot localization. This representation enables RL policies trained in a low-fidelity simulator to deploy in real world with a reduced sim2real gap. Additionally, we propose a spatio-temporal graph to model the interactions between agents and obstacles. Based on the graph, we use attention mechanisms to capture the robot-human, human-human, and human-obstacle interactions. Our method significantly improves navigation performance in both simulated and real-world environments. Video demonstrations can be found at https://sites.google.com/view/constrained-crowdnav/home.
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。
