Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Higher Mental Functions of the Brain: Language

Higher Mental Functions of the Brain: Language

Language is a system of communication that allows the expression of thoughts, ideas, and feelings. The brain processes language in both hemispheres.
Language formation and comprehension take place in the dominant hemisphere. The dominant hemisphere is responsible for understanding the meaning of spoken, written, or sign language, as well as the ability to communicate. For most people, the left hemisphere is the dominant one. The right hemisphere, then, gives tone and emotional context to the...

Sequence Networks of Rotating Machines

Sequence Networks of Rotating Machines

A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...

Language and Cognition

Language and Cognition

Language serves as a bridge between ideas and communication, influencing how individuals perceive and interact with the world. Psychologists have long debated whether language shapes thought or vice versa. This discussion gained grip with Edward Sapir and Benjamin Lee Whorf in the 1940s, who proposed that language determines thought, a concept known as linguistic determinism. They suggested that the vocabulary and structure of a language influence how its speakers think and perceive reality.

Automatic Processing and Automatic Social Behavior

Automatic Processing and Automatic Social Behavior

Automatic processing refers to the cognitive operations that occur without conscious intent or awareness, playing a fundamental role in shaping social cognition and behavior. These processes enable individuals to navigate complex social environments efficiently by relying on mental shortcuts and pre-existing knowledge structures known as schemas. One of the most influential mechanisms underlying automatic processing is priming, which subtly activates mental representations through exposure to...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

An event-based opto-tactile skin.

Frontiers in neuroscience·2026

Same author

IFRA: A Machine Learning-Based Instrumented Fall Risk Assessment Scale Derived from an Instrumented Timed Up and Go Test in Stroke Patients.

Healthcare (Basel, Switzerland)·2026

Same author

TransMA: an explainable multi-modal deep learning model for predicting properties of ionizable lipid nanoparticles in mRNA delivery.

Briefings in bioinformatics·2025

Same author

A Novel Method to Compute the Contact Surface Area Between an Organ and Cancer Tissue.

Journal of imaging·2025

Same author

Innovative 3D method predicts surgery outcomes by calculating real contact surface of renal tumor.

Urologic oncology·2024

Same author

On the role of artificial intelligence in analysing oocytes during in vitro fertilisation procedures.

Artificial intelligence in medicine·2024

Search research articles

Related Experiment Video

Updated: Apr 13, 2026

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

A framework for neurosymbolic robot action planning using large language models.

Alessio Capitanelli¹, Fulvio Mastrogiovanni¹

¹Department of Informatics, Bioengineering, Robotics and Systems Engineering, University of Genoa, Genoa, Italy.

Frontiers in Neurorobotics

|June 19, 2024

Summary

This study introduces Teriyaki, a novel framework that uses Large Language Models (LLMs) for robot task planning. Teriyaki enhances scalability and efficiency in dynamic environments, improving human-robot interaction.

Keywords:

AI GPT PDDL generative human-robot interaction large language model neurosymbolic task planning

More Related Videos

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Related Experiment Videos

Last Updated: Apr 13, 2026

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Area of Science:

Robotics
Artificial Intelligence
Machine Learning

Background:

Symbolic task planning is crucial for robot autonomy but struggles with scalability in dynamic, human-robot collaboration scenarios.
Limitations include slow planning times, long plan lengths, and difficulty adapting to real-world changes, hindering robot efficiency and interaction fluency.

Purpose of the Study:

To bridge the gap between symbolic task planning and machine learning by developing a neurosymbolic task planner using Large Language Models (LLMs).
To leverage LLMs' generative capabilities to overcome the limitations of traditional symbolic planners in complex and dynamic environments.

Main Methods:

Developed a framework named Teriyaki, training GPT-3 (a Large Language Model) into a neurosymbolic task planner compatible with the Planning Domain Definition Language (PDDL).
Utilized LLMs' linear scaling response time and action-by-action plan synthesis for concurrent planning and execution.

Main Results:

Teriyaki achieved a 95.5% problem-solving rate on a test dataset of 1,000 samples.
Generated plans up to 13.5% shorter than traditional symbolic planners.
Reduced average waiting times for plan availability by up to 61.4%.

Conclusions:

Teriyaki demonstrates comparable planning performance to traditional planners while enhancing scalability and efficiency through LLM integration.
The framework enables faster, more adaptable robot task planning, improving human-robot interaction in complex scenarios.