Results 51 to 60 of about 9,156,471 (236)

Scalable Task Planning via Large Language Models and Structured World Representations

Advanced Robotics Research, EarlyView.
This work efficiently combines graph‐based world representations with the commonsense knowledge in Large Language Models to enhance planning techniques for the large‐scale environments that modern robots will need to face. Planning methods often struggle with computational intractability when solving task‐level problems in large‐scale environments ...
Rodrigo Pérez‐Dattari, Zhaoting Li, Robert Babuška, Jens Kober, Cosimo Della Santina   +4 more
wiley   +1 more source

Grounding Large Language Models for Robot Task Planning Using Closed‐Loop State Feedback

Advanced Robotics Research, EarlyView.
BrainBody‐Large Language Model (LLM) introduces a hierarchical, feedback‐driven planning framework where two LLMs coordinate high‐level reasoning and low‐level control for robotic tasks. By grounding decisions in real‐time state feedback, it reduces hallucinations and improves task reliability.
Vineet Bhat, Ali Umut Kaypak, Prashanth Krishnamurthy, Ramesh Karri, Farshad Khorrami   +4 more
wiley   +1 more source

Context-Aware Knowledge Graph Learning for Point-of-Interest Recommendation

ISPRS International Journal of Geo-Information
Existing point-of-interest (POI) recommendation methods often fail to capture complex contextual dependencies and suffer from severe data sparsity in location-based social networks (LBSNs).
Yan Zhou, Di Zhang, Kaixuan Zhou, Pengcheng Han   +3 more
doaj   +1 more source

LLM-Guided Knowledge Distillation for Temporal Knowledge Graph Reasoning

CoRR
Temporal knowledge graphs (TKGs) support reasoning over time-evolving facts, yet state-of-the-art models are often computationally heavy and costly to deploy. Existing compression and distillation techniques are largely designed for static graphs; directly applying them to temporal settings may overlook time-dependent interactions and lead to ...
Wang Xing, Wei Song, Siyu Lin, Chen Wu, Man Wang   +4 more
openaire   +2 more sources

Fine-tuning foundation models for temporal knowledge graph reasoning [PDF]

Foundation models have recently demonstrated strong performance in various knowledge graph reasoning tasks. However, their applicability to temporal knowledge graphs (TKGs), where facts evolve over time, remains underexplored.
Dileo, Manuel, Zignani, Matteo, Minervini, Pasquale; id_orcid   +2 more
core   +1 more source

LLM‐Integrated Human–Robot Interaction System for Microrobots

Advanced Robotics Research, EarlyView.
This paper proposes an LLM‐based control framework for guiding microrobots using human natural language. This framework can convert the natural human speech into safe and executable command sets for reliable navigation in complex environments. The experimental results show high accuracy and robustness in task performance, demonstrating the potential of
Bairong Zhu, Amar Salehi, Tingting Yu
wiley   +1 more source

Intelligent Maintenance Review for Robots: Multimodal Information, Deep Diagnosis and Embodied Artificial Intelligence

Advanced Robotics Research, EarlyView.
This review maps the methods to monitor robots’ health by fusing vibration, sound, control signals, vision, force, and oil information with artificial intelligence. It identifies deep learning, transfer learning, digital twins, and physics‐informed models as key methodological pathways enabling earlier diagnosis, safer human–robot collaboration, and ...
Yuting Qiao, Hongbo Wang, Naipeng Li, Shuo Wang, Yaguo Lei, Tonghai Wu, Junyi Cao   +6 more
wiley   +1 more source

TG-CENET: an improved reasoning model for temporal knowledge graphs based on contrastive history

Journal of King Saud University: Computer and Information Sciences
With the rapid advancement of the Internet and artificial intelligence technologies, knowledge graphs have demonstrated significant potential in data structuring and knowledge reasoning.
Lizhi Miao, Kaiwen Wu, Yi Huang
doaj   +1 more source

JOINT FEATURES-BASED KNOWLEDGE GRAPH COMPLETION [PDF]

International Journal of Intelligent Computing and Information Sciences
Knowledge graphs (KGs) help in resolving data inconsistencies and redundancies by organizing information in a unified structure, paving the way for building scalable, interpretable AI systems, as they provide a transparent way to trace reasoning paths ...
Maha Farghaly, Mostafa Aref, Mahmoud Mounir   +2 more
doaj   +1 more source

Bridging graph structure and knowledge-guided editing for interpretable temporal knowledge graph reasoning

Neural Networks
Temporal knowledge graph reasoning (TKGR) aims to predict future events by inferring missing entities with dynamic knowledge structures. Existing LLM-based reasoning methods prioritize contextual over structural relations, struggling to extract relevant subgraphs from dynamic graphs.
Shiqi Fan, Quanming Yao, Hongyi Nie, Wentao Ma, Zhen Wang, Wen Hua   +5 more
openaire   +4 more sources