Search research articles

Related Concept Videos

Three-Dimensional Force System:Problem Solving

Three-Dimensional Force System:Problem Solving

A three-dimensional force system refers to a scenario in which three forces act simultaneously in three different directions. This type of problem is commonly encountered in physics and engineering, where it is necessary to calculate the resultant force on the system, which can then be used to predict or analyze the behavior of the object or structure under consideration.
To solve a three-dimensional force system, first resolve each force into its respective scalar components. Do this using...

You might also read

Related Articles

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

Sort by

Same author

Direct-Fed Microbials in Diets of Market Pigs Did Not Impact Salmonella Levels, Shiga Toxin-Producing Escherichia coli, Growth Performance, and Carcass Characteristics.

Journal of food protection·2026

Same author

G-RCenterNet: Reinforced CenterNet for Robotic Arm Grasp Detection.

Sensors (Basel, Switzerland)·2025

Same author

New Progress in Artificial Intelligence Algorithm Research Based on Big Data Processing of IOT Systems on Intelligent Production Lines.

Computational intelligence and neuroscience·2022

Same journal

Analysis of strength degradation of coal and rock masses and stability of mined areas under long term immersion environment.

PloS one·2026

Same journal

Biogenic Silver-Selenium nanocomposite with anticancer activity and potent efficacy against vancomycin-resistant Staphylococcus aureus.

PloS one·2026

Same journal

Preparation and physicochemical characterization of a biodegradable chitosan/carboxymethyl cellulose hydrogel synthesized in NaOH/urea medium.

PloS one·2026

Same journal

Action-guilt, survivor-guilt, and depression in combat-related PTSD.

PloS one·2026

Same journal

Explainable machine learning for predicting activities of daily living at discharge in stroke patients: A retrospective study using SHAP interpretability.

PloS one·2026

Same journal

Deep learning based two-way feature depiction model for brain tumor detection.

PloS one·2026

See all related 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

Search research articles

Related Experiment Video

Updated: Dec 25, 2025

Author Spotlight: Enhancing Post-Stroke Upper Limb Rehabilitation with Robotic Technologies for Improved Motor Recovery and Functional Outcomes

Author Spotlight: Enhancing Post-Stroke Upper Limb Rehabilitation with Robotic Technologies for Improved Motor Recovery and Functional Outcomes

Published on: September 6, 2024

Multi-agent deep reinforcement learning-based robotic arm assembly research.

Guohua Cao¹, Jimeng Bai¹

¹School of Mechanical and Electrical Engineering, Changchun University of Science and Technology, Changchun, China.

|February 18, 2025

Summary

This summary is machine-generated.

This study introduces a multi-agent reinforcement learning approach for robotic arm assembly, improving convergence and performance in complex shaft-hole tasks. The novel method enhances adaptability and stability in robotic assembly operations.

More Related Videos

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

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

Published on: October 14, 2017

Investigating Motor Skill Learning Processes with a Robotic Manipulandum

Investigating Motor Skill Learning Processes with a Robotic Manipulandum

Published on: February 12, 2017

Related Experiment Videos

Last Updated: Dec 25, 2025

Author Spotlight: Enhancing Post-Stroke Upper Limb Rehabilitation with Robotic Technologies for Improved Motor Recovery and Functional Outcomes

Author Spotlight: Enhancing Post-Stroke Upper Limb Rehabilitation with Robotic Technologies for Improved Motor Recovery and Functional Outcomes

Published on: September 6, 2024

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

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

Published on: October 14, 2017

Investigating Motor Skill Learning Processes with a Robotic Manipulandum

Investigating Motor Skill Learning Processes with a Robotic Manipulandum

Published on: February 12, 2017

Area of Science:

Robotics
Artificial Intelligence
Control Systems

Background:

Single-agent algorithms struggle with convergence and performance in complex robotic arm assembly tasks due to variability.
Robotic arm assembly, particularly shaft-hole tasks, requires robust and adaptive control strategies.

Purpose of the Study:

To propose and evaluate a multi-agent reinforcement learning (MARL) algorithm for robotic arm shaft-hole assembly.
To enhance the convergence speed, stability, and adaptability of robotic assembly processes, focusing on square shaft-hole configurations.

Main Methods:

Analysis of shaft-hole assembly stages: hole-seeking, alignment, and insertion.
Integration of a novel reward function with the decoupled multi-agent deterministic deep deterministic policy gradient (DMDDPG) algorithm.
Development of a simulation environment in Gazebo for modeling robotic arm assembly with circular and square shaft-holes.

Main Results:

The proposed MARL algorithm, dividing the robotic arm into multi-agents (first three and last three joints), showed improved performance.
Demonstrated enhanced adaptability and faster, more stable convergence in shaft-hole assembly simulations.
Successfully modeled and addressed challenges in square shaft-hole assembly, a complex scenario.

Conclusions:

Multi-agent reinforcement learning offers a promising solution for improving robotic arm assembly tasks.
The DMDDPG-based approach enhances the efficiency and reliability of robotic assembly, particularly in intricate tasks like square shaft-hole insertion.
The developed simulation framework validates the effectiveness of the MARL strategy for real-world robotic assembly applications.