摘要
一位新闻记者-机器人与机器学习的工作人员新闻编辑每日新闻-机器人的研究发现-机器人CS和自动化在一份新的报告中讨论。根据来自马萨诸塞州剑桥的新闻报道，研究州D的NewsRx记者Ori Ginating说，“这封信提出了一种在任意的印度和户外环境中构建三维场景图的方法。这种扩展具有挑战性；描述户外环境的conc层次结构比室内复杂，手工定义这种层次结构既耗时又不可扩展。”这项研究的财政支持来自ARL DCIST计划。我们的新闻编辑从麻省理工学院的研究中获得了一句话：“此外，训练数据的缺乏阻碍了基于学习的工具在室内环境中的广泛应用。为了应对这些挑战，我们提出了两个新的扩展。首先，我们开发了一个定义室内和室外机器人操作相关概念和关系的空间本体的方法。特别是，其次，我们利用空间本体构建三维场景图，使用逻辑张量网络(LTN)添加逻辑规则或公理(例如，‘海滩包含沙子’)，在训练时间提供额外的监督信号，减少对标记数据的需求，提供更好的预测，甚至允许预测训练时看不见的概念。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Research findings on Robotics - Roboti cs and Automation are discussed in a new report. According to news reporting ori ginating from Cambridge, Massachusetts, by NewsRx correspondents, research state d, "This letter proposes an approach to build 3D scene graphs in arbitrary indoo r and outdoor environments. Such extension is challenging; the hierarchy of conc epts that describe an outdoor environment is more complex than for indoors, and manually defining such hierarchy is time-consuming and does not scale." Financial support for this research came from ARL DCIST Program. Our news editors obtained a quote from the research from the Massachusetts Insti tute of Technology, "Furthermore, the lack of training data prevents the straigh tforward application of learning-based tools used in indoor settings. To address these challenges, we propose two novel extensions. First, we develop methods to build a spatial ontology defining concepts and relations relevant for indoor an d outdoor robot operation. In particular, we use a Large Language Model (LLM) to build such an ontology, thus largely reducing the amount of manual effort requi red. Second, we leverage the spatial ontology for 3D scene graph construction us ing Logic Tensor Networks (LTN) to add logical rules, or axioms (e.g., 'a beach contains sand'), which provide additional supervisory signals at training time t hus reducing the need for labelled data, providing better predictions, and even allowing predicting concepts unseen at training time."
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