Results 121 to 130 of about 177,158 (279)

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

Piezoresistivity Enhancement by Graphite Flake Alignment in Thin Composite Films for Dielectric Elastomer Switches

Advanced Robotics Research, EarlyView.
This article presents dielectric elastomer switch (DES) materials, based on composite thin films. Alignment of graphite flakes due to their physical confinement within the thin films lead to much stronger piezoresistive responses than bulk composites, while their durability exceeds that of conventional liquid‐based DES materials.
Lingyu Liu, Thomas Kister, Bart‐Jan Niebuur, Tobias Kraus   +3 more
wiley   +1 more source

A Relation-Specific Entropy-Based Ensemble Approach for Knowledge Graph Embedding

IEEE Access
Knowledge Graph Embedding (KGE) aims to represent entities and relationships from knowledge graphs (KGs) in vector spaces. Existing KGE methods often focus narrowly on specific relation patterns or employ ensemble weights based on the prediction scores ...
Hwawoo Jeon, Yoonseob Lim, Yong Suk Choi
doaj   +1 more source

An inductive knowledge graph embedding via combination of subgraph and type information. [PDF]

Sci Rep, 2023
Liu H, Chen Y, He P, Zhang C, Wu H, Zhang J.   +5 more
europepmc   +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

Weighted Knowledge Graph Embedding

Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2023
Zhao Zhang, Zhanpeng Guan, Fuwei Zhang, Fuzhen Zhuang, Zhulin An, Fei Wang, Yongjun Xu   +6 more
openaire   +1 more source

Towards leveraging explicit negative statements in knowledge graph embeddings

Web Semantics
Knowledge Graphs are used in various domains to represent knowledge about entities and their relations. In the vast majority of cases, they capture what is known to be true about those entities, i.e., positive statements, while the Open World Assumption ...
Rita T. Sousa, Catia Pesquita, Heiko Paulheim   +2 more
doaj   +1 more source

Durability of Soft Pneumatic Actuators: A Review and Benchmarking Protocol

Advanced Robotics Research, EarlyView.
Lack of durability is a key challenge hindering the broad scale adoption of soft pneumatic actuators (SPAs) in automation industries. This review provides a comprehensive overview of existing research on SPA durability, introduces a standardized durability benchmarking protocol to consolidate the testing of SPAs, and outlines promising directions for ...
Dickson Chiu Yu Wong, Yucheng Luo, Rob Bernardus Nicolaas Scharff   +2 more
wiley   +1 more source

Marie and BERT-A Knowledge Graph Embedding Based Question Answering System for Chemistry. [PDF]

ACS Omega, 2023
Zhou X, Zhang S, Agarwal M, Akroyd J, Mosbach S, Kraft M.   +5 more
europepmc   +1 more source

Data‐Driven Bulldozer Blade Control for Autonomous Terrain Leveling

Advanced Robotics Research, EarlyView.
A simulation‐driven framework for autonomous bulldozer leveling is presented, combining high‐fidelity terramechanics simulation with a neural‐network‐based reduced‐order model. Gradient‐based optimization enables efficient, low‐level blade control that balances leveling quality and operation time.
Harry Zhang, Ganesh Arivoli, Huzaifa Unjhawala, Luning Bakke, Radu Serban, Dan Negrut   +5 more
wiley   +1 more source