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

Control Systems01:10

Control Systems

Control systems are everywhere in contemporary society, influencing diverse applications from aerospace to automated manufacturing. These systems can be found naturally within biological processes, such as blood sugar regulation and heart rate adjustment in response to stress, as well as in man-made systems like elevators and automated vehicles. A control system is essentially a network of subsystems and processes that collaboratively convert specific inputs into desired outputs.
At the heart...
Control Systems: Applications01:25

Control Systems: Applications

Electrical engineering plays a pivotal role in our daily lives, with control systems at the heart of many applications, from home appliances to sophisticated space shuttles. Control systems manage and regulate the behavior of devices and processes, ensuring they function safely, correctly, and efficiently.
In modern vehicles, control systems manage various functions to enhance performance and safety. The steering wheel and accelerator are primary inputs in a car's control system. The direction...
Open and closed-loop control systems01:17

Open and closed-loop control systems

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 and...
A Single-Component System01:24

A Single-Component System

In the field of chemistry, the terms "component" and "phase" hold significant importance. A component refers to a chemically distinct substance in a system that has specific properties. It is chemically homogeneous, meaning it has the same properties throughout. For example, in a mixture of salt and water, both salt and water are considered separate components because they have different chemical properties.On the other hand, a phase is a form of matter that has a consistent chemical...
PI Controller: Design01:24

PI Controller: Design

Proportional Integral (PI) controllers are a fundamental component in modern control systems, widely used to enhance performance and mitigate steady-state errors. They are particularly effective in applications such as automatic brightness adjustment on smartphones, where they excel at mitigating steady-state errors for step-function inputs. Unlike PD controllers, which require time-varying errors to function optimally, PI controllers leverage their integral component to address residual...
Control System Problem01:21

Control System Problem

In an open-loop system, such as a basic thermostat, the poles of the transfer function influence the system's response but do not determine its stability. However, when feedback is introduced to form a closed-loop system, such as an advanced thermostat that adjusts heating based on room temperature, stability is governed by the new poles of the closed-loop transfer function.
When forming a closed-loop system, issues can arise if the poles cross into the unstable region, leading to potential...

You might also read

Related Articles

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

Sort by
Same author

Oncilla Robot: A Versatile Open-Source Quadruped Research Robot With Compliant Pantograph Legs.

Frontiers in robotics and AI·2021
Same author

A Differentiable Physics Engine for Deep Learning in Robotics.

Frontiers in neurorobotics·2019
Same author

Online Training of an Opto-Electronic Reservoir Computer Applied to Real-Time Channel Equalization.

IEEE transactions on neural networks and learning systems·2017
Same author

Embodiment of Learning in Electro-Optical Signal Processors.

Physical review letters·2016
Same author

Trainable hardware for dynamical computing using error backpropagation through physical media.

Nature communications·2015
Same author

On learning navigation behaviors for small mobile robots with reservoir computing architectures.

IEEE transactions on neural networks and learning systems·2015
Same journal

Learning under constraints: a theoretical framework for comparing resource-constrained learning in biological and artificial systems.

Frontiers in computational neuroscience·2026
Same journal

MsGCN: a multi-stream graph convolutional network for multiband PLV graph fusion in EEG-based biometric identification.

Frontiers in computational neuroscience·2026
Same journal

AI-driven neuroanalytic modeling for mental health: multichannel CNN-based autism spectrum disorder detection via facial pattern analysis.

Frontiers in computational neuroscience·2026
Same journal

Modeling multiscale neural dynamics for EEG-based emotion recognition using an attentive wavelet-transformer framework.

Frontiers in computational neuroscience·2026
Same journal

New directions for complex systems in contemporary neuroscience: a morphodynamic and emergent function approach.

Frontiers in computational neuroscience·2026
Same journal

NMDA receptor kinetics drive distinct routes to chaotic firing in pyramidal neurons.

Frontiers in computational neuroscience·2026
See all related articles

Related Experiment Video

Updated: May 9, 2026

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

MACOP modular architecture with control primitives.

Tim Waegeman1, Michiel Hermans, Benjamin Schrauwen

  • 1Department of Electronics and Information Systems, Ghent University Ghent, Belgium.

Frontiers in Computational Neuroscience
|July 27, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces a modular architecture with control primitives (MACOP) for robot motor control. MACOP unsupervisedly mixes primitive controllers for adaptive and skillful robot movement, compensating for dynamic effects.

Keywords:
MOSAICecho state networksmotor controlmotor primitivesmovement primitivesreservoir computingrobot control

More Related Videos

Standardized Modular Assembly of Polycistronic Operons with Modular Cloning (MoClo) using the In-Cloning toolkit
06:28

Standardized Modular Assembly of Polycistronic Operons with Modular Cloning (MoClo) using the In-Cloning toolkit

Published on: September 2, 2025

Related Experiment Videos

Last Updated: May 9, 2026

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

Standardized Modular Assembly of Polycistronic Operons with Modular Cloning (MoClo) using the In-Cloning toolkit
06:28

Standardized Modular Assembly of Polycistronic Operons with Modular Cloning (MoClo) using the In-Cloning toolkit

Published on: September 2, 2025

Area of Science:

  • Robotics
  • Control Theory
  • Computational Neuroscience

Background:

  • Human and animal motor skills rely on modular control hierarchies combining movement primitives.
  • Engineers seek adaptive and skillful robot control inspired by biological motor control.

Purpose of the Study:

  • To propose a novel modular architecture with control primitives (MACOP) for robot motor control.
  • To develop an unsupervised method for mixing primitive controllers to achieve complex movements.
  • To evaluate MACOP's performance in generating robot arm trajectories and compensating for dynamic effects.

Main Methods:

  • MACOP utilizes a set of specialized controllers, each active in a subregion of the robot's joint and task-space.
  • A mixing mechanism, guided by desired properties, enables unsupervised emergence of control primitives.
  • The architecture was evaluated on a numerical robot arm model trained for trajectory generation.

Main Results:

  • MACOP successfully generated desired robot arm trajectories.
  • The study investigated the impact of the number of controllers on tracking performance.
  • MACOP demonstrated compensation for dynamic effects arising from fixed control rates and robot inertia.

Conclusions:

  • MACOP offers an effective approach to unsupervised learning of complex motor skills in robots.
  • The modular architecture provides adaptive and skillful control, inspired by biological systems.
  • MACOP's ability to mix primitive controllers enhances robotic movement capabilities.