首页|Studies from Massachusetts Institute of Technology Further Understanding of Robo tics and Automation (Indoor and Outdoor 3d Scene Graph Generation Via Language-e nabled Spatial Ontologies)

Studies from Massachusetts Institute of Technology Further Understanding of Robo tics and Automation (Indoor and Outdoor 3d Scene Graph Generation Via Language-e nabled Spatial Ontologies)

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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."

CambridgeMassachusettsUnited StatesNorth and Central AmericaRobotics and AutomationRoboticsMassachusetts Ins titute of Technology

2024

Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

ISSN:
年,卷(期):2024.(Jun.18)