arXiv AI June 9, 2026 By Jiangtao Shuai, Zongxiong Chen, Manfred Hauswirth, Sonja Schimmler

From USD Scenes to Knowledge Graphs: Zero-Shot Ontology Grounding with LLMs

arXiv:2606. 09134v1 Announce Type: cross Abstract: Constructing knowledge graphs from 3D simulation scenes is essential for robot task reasoning, but the key bottleneck, grounding scene objects to formal ontology classes, still relies on manually curated dictionaries that are brittle and do not generalize across assets.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv AI

Jun 30

Low-cost concept-based localized explanations: How far can we get with training-free approaches?

arXiv:2606. 29069v1 Announce Type: new Abstract: Concept-based Explainable AI (C-XAI) seeks human-understandable explanations grounded in semantic concepts, yet validation is limited by the scarcity of fine-grained concept annotations.

By Darian Fern\'andez-Guti\'errez, Rafael Bello, Marilyn Bello, Natalia D\'iaz-Rodr\'iguez

llms rag multimodal

arXiv AI

Jul 21

Spatiotemporal Knowledge Graphs as Persistent Scene Memory for Embodied Question Answering

arXiv:2510. 01483v3 Announce Type: replace-cross Abstract: Vision-language models (VLMs) demonstrate strong image-level scene understanding, but reasoning over long egocentric video remains costly: because VLMs maintain no persistent memory or explicit spatial representation, all sampled frames must be re-processed for every new query.

By Mohamad Al Mdfaa, Svetlana Lukina, Timur Akhtyamov, Arthur Nigmatzyanov, Dmitrii Nalberskii, Sergey Zagoruyko, Gonzalo Ferrer

llms agents nlp robotics multimodal benchmarks

arXiv AI

Jul 8

OpenGround: Planning-based Online Perception for Open-World 3D Visual Grounding

arXiv:2512. 23020v3 Announce Type: replace-cross Abstract: 3D visual grounding aims to locate objects based on natural language descriptions in 3D scenes.

By Wenyuan Huang, Zhenyu Zhang, Zhao Wang, Zhou Wei, Ting Huang, Fang Zhao, Jian Yang

llms benchmarks

arXiv AI

Jul 16

HRO: Hierarchical Room-to-Object Framework for Zero-Shot Object Goal Navigation with Large Language Models

arXiv:2607. 13072v1 Announce Type: cross Abstract: Zero-shot object-goal navigation aims to enable an intelligent agent to explore and navigate to objects of unknown categories in an unfamiliar environment without specific target training.

By Luyuan Jia, Yinfeng Yu

llms agents

arXiv AI

Jun 17

Extracting Semantics: LLM-Guided Automatic Population of Robot Ontology from URDF

arXiv:2606. 17073v1 Announce Type: cross Abstract: While commonsense knowledge may suffice for virtual agents, embodied robots interacting with humans require grounded and semantically rich representations of both their environment and their own physical embodiment.

By Bastien Dussard (LAAS-RIS, LAAS), Guillaume Sarthou (LAAS-RIS, LAAS)

llms agents robotics

arXiv AI

Jun 30

GROW$^2$: Grounding Which and Where for Robot Tool Use

arXiv:2606. 30632v1 Announce Type: cross Abstract: Can the robot use a plate to cut a cake if no knife is available?

By Yuhong Deng, Yuyao Liu, David Hsu

llms agents robotics multimodal benchmarks

Related stories

Low-cost concept-based localized explanations: How far can we get with training-free approaches?

Spatiotemporal Knowledge Graphs as Persistent Scene Memory for Embodied Question Answering

OpenGround: Planning-based Online Perception for Open-World 3D Visual Grounding

HRO: Hierarchical Room-to-Object Framework for Zero-Shot Object Goal Navigation with Large Language Models

Extracting Semantics: LLM-Guided Automatic Population of Robot Ontology from URDF

GROW$^2$: Grounding Which and Where for Robot Tool Use