SGN-CIRL：基于场景图并结合课程学习、模仿学习与强化学习的导航方法
SGN-CIRL: Scene Graph-based Navigation with Curriculum, Imitation, and Reinforcement Learning
AI总结：
本文提出SGN-CIRL框架，将可学习的开放词汇3D场景图与模仿学习和课程学习相结合，用于无地图强化学习机器人导航，实验表明其显著提升了困难导航任务的成功率。
摘要：
3D场景图对物体之间的空间关系进行建模，使智能体能够在部分可观测环境中高效导航并预测目标物体的位置。本文提出一个名为SGN-CIRL（基于3D场景图的强化学习导航）的原创框架，用于基于无地图强化学习的机器人导航，并具有可学习的开放词汇3D场景图表示。为了加速并稳定基于强化学习算法的训练，该框架还采用了模仿学习和课程学习。前者使智能体能够从示范中学习，后者通过将任务复杂度从简单场景逐步提高到更高级场景来组织训练过程。在Isaac Sim环境中进行的数值实验表明，在强化学习中使用3D场景图显著提高了困难导航情况下的成功率。代码已开源，可在https://github.com/Xisonik/Aloha_graph获取。
英文摘要：
The 3D scene graph models spatial relationships between objects, enabling the agent to efficiently navigate in a partially observable environment and predict the location of the target object.This paper proposes an original framework named SGN-CIRL (3D Scene Graph-Based Reinforcement Learning Navigation) for mapless reinforcement learning-based robot navigation with learnable representation of open-vocabulary 3D scene graph. To accelerate and stabilize the training of reinforcement learning-based algorithms, the framework also employs imitation learning and curriculum learning. The first one enables the agent to learn from demonstrations, while the second one structures the training process by gradually increasing task complexity from simple to more advanced scenarios. Numerical experiments conducted in the Isaac Sim environment showed that using a 3D scene graph for reinforcement learning significantly increased the success rate in difficult navigation cases. The code is open-sourced and available at: https://github.com/Xisonik/Aloha\_graph.
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。
