论文标题
使用场景图进行推理，用于在部分可观察性下进行机器人计划
Reasoning with Scene Graphs for Robot Planning under Partial Observability
论文作者
论文摘要
在部分可观察的域中计划机器人规划很困难，因为机器人需要同时估算当前状态和计划行动。当域包含许多对象时，关于对象及其关系的推理使机器人计划变得更加困难。在本文中，我们开发了一种称为机器人计划（SARP）场景分析的算法，该算法使机器人能够通过视觉上下文信息来理解不确定性下的长期目标。 SARP构造场景图，是对象及其关系的分类表示，使用从不同位置捕获的图像，以及与它们的理由在部分可观察性下启用上下文感知的机器人计划。在模拟中使用多个3D环境进行了实验，而由真实机器人收集的数据集进行了实验。与标准的机器人计划和场景分析方法相比，在目标搜索域中，SARP提高了任务完成的效率和准确性。可以在https://tinyurl.com/sarp22上找到补充材料
Robot planning in partially observable domains is difficult, because a robot needs to estimate the current state and plan actions at the same time. When the domain includes many objects, reasoning about the objects and their relationships makes robot planning even more difficult. In this paper, we develop an algorithm called scene analysis for robot planning (SARP) that enables robots to reason with visual contextual information toward achieving long-term goals under uncertainty. SARP constructs scene graphs, a factored representation of objects and their relations, using images captured from different positions, and reasons with them to enable context-aware robot planning under partial observability. Experiments have been conducted using multiple 3D environments in simulation, and a dataset collected by a real robot. In comparison to standard robot planning and scene analysis methods, in a target search domain, SARP improves both efficiency and accuracy in task completion. Supplementary material can be found at https://tinyurl.com/sarp22
