Social navigation datasets are necessary to assess social navigation algorithms and train machine learning algorithms. Most of the currently available datasets target pedestrians' movements as a pattern to be replicated by robots. It can be argued that one of the main reasons for this to happen is that compiling datasets where real robots are manually controlled, as they would be expected to behave when moving, is a very resource-intensive task. Another aspect that is often missing in datasets is symbolic information that could be relevant, such as human activities, relationships or interactions. Unfortunately, the available datasets targeting robots and supporting symbolic information are restricted to static scenes. This paper argues that simulation can be used to gather social navigation data in an effective and cost-efficient way and presents a toolkit for this purpose. A use case studying the application of graph neural networks to create learned control policies using supervised learning is presented as an example of how it can be used.

中文翻译：

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生成社交导航数据集的工具包

社交导航数据集是评估社交导航算法和训练机器学习算法所必需的。大多数当前可用的数据集都将行人的运动作为机器人复制的模式。可以说，发生这种情况的主要原因之一是，在真实机器人被手动控制的情况下编译数据集，因为它们在移动时会表现出预期的行为，这是一项非常耗费资源的任务。数据集中经常缺少的另一个方面是可能相关的符号信息，例如人类活动、关系或交互。不幸的是，针对机器人和支持符号信息的可用数据集仅限于静态场景。本文认为，模拟可用于以有效且具有成本效益的方式收集社交导航数据，并为此提供了一个工具包。一个用例研究了图神经网络的应用，以使用监督学习创建学习控制策略，作为如何使用它的示例。