Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Neural Circuits01:25

Neural Circuits

Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
Introduction to Learning01:18

Introduction to Learning

Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
In contrast to learned behaviors, unlearned behaviors such as crying, sexual...

You might also read

Related Articles

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

Sort by
Same author

Enhancement of long-horizon task planning via active and passive modification in large language models.

Scientific reports·2025
Same author

Assessment of Tail-Cutting in Frozen Albacore (<i>Thunnus alalunga</i>) Through Ultrasound Inspection and Chemical Analysis.

Foods (Basel, Switzerland)·2024
Same author

Detecting hand joint ankylosis and subluxation in radiographic images using deep learning: A step in the development of an automatic radiographic scoring system for joint destruction.

PloS one·2023
Same author

Tactile Transfer Learning and Object Recognition With a Multifingered Hand Using Morphology Specific Convolutional Neural Networks.

IEEE transactions on neural networks and learning systems·2022
Same author

Emergence of sensory attenuation based upon the free-energy principle.

Scientific reports·2022
Same author

Efficient multitask learning with an embodied predictive model for door opening and entry with whole-body control.

Science robotics·2022

Related Experiment Video

Updated: Jul 15, 2026

Designing and Implementing Nervous System Simulations on LEGO Robots
10:34

Designing and Implementing Nervous System Simulations on LEGO Robots

Published on: May 25, 2013

Development of a Basic Educational Kit for Robotic System with Deep Neural Networks.

Momomi Kanamura1, Kanata Suzuki1,2, Yuki Suga1

  • 1Department of Intermedia Art and Science, School of Fundamental Science and Engineering, Waseda University, Tokyo 169-8050, Japan.

Sensors (Basel, Switzerland)
|June 2, 2021
PubMed
Summary

This study introduces an educational kit for beginners to learn robotics and deep neural networks (DNNs). The kit simplifies DNN integration into robotic systems through a structured learning approach.

Keywords:
deep neural networkseducational kitrobot middleware

More Related Videos

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

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

Published on: March 2, 2015

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
11:53

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Published on: October 14, 2017

Related Experiment Videos

Last Updated: Jul 15, 2026

Designing and Implementing Nervous System Simulations on LEGO Robots
10:34

Designing and Implementing Nervous System Simulations on LEGO Robots

Published on: May 25, 2013

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

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

Published on: March 2, 2015

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
11:53

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Published on: October 14, 2017

Area of Science:

  • Robotics
  • Machine Learning
  • Artificial Intelligence

Background:

  • Deep neural networks (DNNs) show promise in robotics but lack systematic integration and beginner-friendly tools.
  • Current robotics and DNN research has not been effectively synthesized for educational purposes.

Purpose of the Study:

  • To develop a basic educational kit for robotic system development using DNNs.
  • To provide beginners with an accessible platform for learning robotics and machine learning, specifically DNNs.
  • To ensure the kit is easy to understand, facilitates experiential learning, and has broad applicability.

Main Methods:

  • Analyzed deep neural network (DNN) research and development (R&D) to define key learning stages: data collection (DC), machine learning (ML), and task execution (TE).
  • Designed a hierarchical system flow enabling individual execution of DC, ML, and TE steps.
  • Implemented the system flow for a physical robotic grasping system using robotics middleware.

Main Results:

  • The proposed educational kit facilitates the integration of DNNs into robotic systems for beginners.
  • The hierarchical system flow (DC, ML, TE) was successfully implemented and validated on a robotic grasping task.
  • Demonstrated the system's adaptability to diverse hardware, sensor inputs, and robot tasks.

Conclusions:

  • The developed educational kit effectively bridges the gap between robotics and DNNs for novice learners.
  • The structured approach simplifies complex DNN concepts for practical robotics applications.
  • The system's modular design ensures versatility and potential for wider adoption in robotics education.