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

Planar Rigid-Body Motion01:22

Planar Rigid-Body Motion

862
Understanding the movement of a rigid body in planar motion involves recognizing that every particle within this body is traversing a path that maintains a consistent distance from a specific plane. This concept is fundamental in the study of physics and mechanical engineering, and it allows us to comprehend better how objects move in space.
Planar motion is typically divided into three distinct categories. The first is rectilinear translation, demonstrated by a subway train that moves along...
862
One-Degree-of-Freedom System01:24

One-Degree-of-Freedom System

710
In mechanical engineering, one-degree-of-freedom systems form the basis of a wide range of electrical and mechanical components. Using these models, engineers can predict the behavior of various parts in a larger system, which gives them insight into how different forces interact with each other.
A one-degree-of-freedom system is defined by an independent variable that determines its state and behavior. One example of a one-degree-of-freedom system is a simple harmonic oscillator, such as a...
710
Hierarchy of Motor Control01:18

Hierarchy of Motor Control

5.7K
The hierarchy of motor control refers to the different levels of organization and processing involved in controlling movement in the body. These levels range from higher cortical areas involved in planning and decision-making to lower spinal cord reflexes that respond automatically to external stimuli.
5.7K
Muscle Coordination and Action01:24

Muscle Coordination and Action

2.9K
Muscle coordination is a complex and finely tuned process essential for smooth and purposeful movements like flexion, extension, adduction, abduction, and rotation. The human body orchestrates the actions of various muscles working in concert, each with a specific role. Four functional types describe how muscles work together: agonist, antagonist, synergist, and fixator.
Agonists
Agonist muscles, often called prime movers, are the primary muscles responsible for producing a specific movement....
2.9K
Three-Dimensional Force System:Problem Solving01:30

Three-Dimensional Force System:Problem Solving

1.3K
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...
1.3K
Open and closed-loop control systems01:17

Open and closed-loop control systems

1.4K
Control systems are foundational elements in automation and engineering. They are broadly categorized into open-loop and closed-loop systems. These classifications hinge on the presence or absence of feedback mechanisms, significantly influencing the system's performance, complexity, and application.
An open-loop control system operates without feedback from the output. It consists of two primary elements: the controller and the controlled process. The controller receives an input signal...
1.4K

You might also read

Related Articles

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

Sort by
Same author

Low-hygroscopic solvents enable ambient blade coating of efficient perovskite solar cells.

Nature communications·2026
Same author

Polymerized Surface Passivation for Stable and Efficient Inverted Perovskite Solar Cells.

Advanced materials (Deerfield Beach, Fla.)·2026
Same author

Active Dataset Distillation via Dual-Space Informative Matching.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same author

A Cattle Behavior Recognition Method Based on Graph Neural Network Compression on the Edge.

Animals : an open access journal from MDPI·2026
Same author

Explosive Output to Enhance Jumping Ability: A Variable Reduction Ratio Design Paradigm for Humanoid Robot Knee Joint.

Biomimetics (Basel, Switzerland)·2026
Same author

Progressive Feature Encoding With Background Perturbation Learning for Ultra-Fine-Grained Visual Categorization.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026

Related Experiment Video

Updated: Dec 17, 2025

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

12.1K

A Multitasking-Oriented Robot Arm Motion Planning Scheme Based on Deep Reinforcement Learning and Twin

Chuzhao Liu1,2, Junyao Gao1,2, Yuanzhen Bi1,2

  • 1Intelligent Robotics Institute, School of Mechatronical Engineering, Beijing Institute of Technology, 5 Nandajie, Zhongguancun, Haidian, Beijing 100081, China.

Sensors (Basel, Switzerland)
|June 25, 2020
PubMed
Summary

This study introduces a novel deep reinforcement learning (DRL) and digital twin approach for controlling humanoid robot arms. The method enables rapid, stable, and diverse multitasking for robots like the BHR-6, improving learning efficiency.

Keywords:
deep reinforcement learninghumanoid robottwin synchro-control

More Related Videos

Robotic Mirror Therapy System for Functional Recovery of Hemiplegic Arms
10:32

Robotic Mirror Therapy System for Functional Recovery of Hemiplegic Arms

Published on: August 15, 2016

15.9K
Author Spotlight: Enhancing Upper Limb Rehabilitation in Stroke Patients Through Advanced Robotic and Neuromodulation Technologies
05:28

Author Spotlight: Enhancing Upper Limb Rehabilitation in Stroke Patients Through Advanced Robotic and Neuromodulation Technologies

Published on: October 11, 2024

1.0K

Related Experiment Videos

Last Updated: Dec 17, 2025

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

12.1K
Robotic Mirror Therapy System for Functional Recovery of Hemiplegic Arms
10:32

Robotic Mirror Therapy System for Functional Recovery of Hemiplegic Arms

Published on: August 15, 2016

15.9K
Author Spotlight: Enhancing Upper Limb Rehabilitation in Stroke Patients Through Advanced Robotic and Neuromodulation Technologies
05:28

Author Spotlight: Enhancing Upper Limb Rehabilitation in Stroke Patients Through Advanced Robotic and Neuromodulation Technologies

Published on: October 11, 2024

1.0K

Area of Science:

  • Robotics and Artificial Intelligence
  • Humanoid Robot Control Systems
  • Digital Twin Technology Applications

Background:

  • Humanoid robots with arms are crucial for public acceptance and present significant robotics challenges.
  • Digital twin technology aligns with Industry 4.0 and Made in China 2025 initiatives.
  • Existing methods struggle with rapid, diverse, and stable motion planning for humanoid robot arms.

Purpose of the Study:

  • To propose a combined deep reinforcement learning (DRL) and digital twin scheme for controlling humanoid robot arms.
  • To develop a multitasking-oriented training approach for rapid and stable motion planning.
  • To enhance DRL training efficiency by incorporating a priori knowledge and improving reward functions.

Main Methods:

  • A Twin Synchro-Control (TSC) scheme integrating DRL with digital twin technology for robot arm control.
  • Development of a data acquisition system to generate human joint angle data for training.
  • Utilizing human joint angle data to refine the reward function of the Deep Deterministic Policy Gradient (DDPG) algorithm.
  • Application to the BHR-6 humanoid robot model for simulation-based training.

Main Results:

  • The proposed DRL with TSC scheme enables fast and diverse multitasking for humanoid robot arms.
  • The approach effectively addresses the sparse reward problem in DRL using collected human motion data.
  • Simulations demonstrated superior learning stability and convergence speed compared to vanilla DDPG.
  • The trained humanoid robot successfully performed tasks not achievable with existing training methods.

Conclusions:

  • The integration of DRL and digital twin technology offers a powerful solution for humanoid robot arm control.
  • The TSC scheme with a priori knowledge significantly improves training efficiency and task performance.
  • This method facilitates rapid adaptation and multi-task capability in complex humanoid robots.