用3D场景图提升机器人导航成功率，结合模仿与课程学习加速训练。
SGN-CIRL: Scene Graph-based Navigation with Curriculum, Imitation, and Reinforcement Learning
- 基于3D场景图建模物体空间关系，支持开放词汇表的可学习表示。
- 在Isaac Sim中实验显示复杂导航任务成功率显著提升。
- 适合研究视觉导航、强化学习与多模态感知融合的开发者。
3D场景图能够建模物体间的空间关系，使智能体在部分可观测环境中高效导航并预测目标位置。本文提出一种名为SGN-CIRL（基于3D场景图的强化学习导航）的新型框架，用于无需地图的强化学习机器人导航，其核心是可学习的开放词汇表3D场景图表示。为加速并稳定强化学习算法的训练，框架还引入了模仿学习与课程学习：前者使智能体从示范中学习，后者通过逐步增加任务难度来组织训练过程。在Isaac Sim环境中的数值实验表明，使用3D场景图进行强化学习能显著提高复杂导航任务的成功率。代码已开源，地址为：https://github.com/Xisonik/Aloha_graph。
原文摘要 · Abstract (English)
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.
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