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

Mechanical Systems01:22

Mechanical Systems

897
Mechanical systems are analogous to to electrical networks where springs and masses play similar roles to inductors and capacitors, respectively. A viscous damper in mechanical systems functions similarly to a resistor in electrical networks, dissipating energy. The forces acting on a mass in such systems include an applied force in the direction of motion, counteracted by forces from the spring, a viscous damper, and the mass's acceleration. This interplay of forces is mathematically...
897
Hierarchy of Motor Control01:18

Hierarchy of Motor Control

5.5K
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.
5.5K
Control Systems01:10

Control Systems

1.7K
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...
1.7K
Control Systems: Applications01:25

Control Systems: Applications

1.2K
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...
1.2K
Stereotype Content Model02:16

Stereotype Content Model

13.0K
The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...
13.0K
Machines: Problem Solving II01:30

Machines: Problem Solving II

787
Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. Consider a lifting tong carrying a 100 kg load. It comprises movable sections DAF and CBG linked together with member AB.
787

You might also read

Related Articles

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

Sort by
Same author

Catecholamine precursor modulation of human exploration: Evidence from a large gender-balanced sample.

PLoS computational biology·2026
Same author

The earlier you know, the smoother you act: anticipatory control in solo and dyadic juggling.

Experimental brain research·2026
Same author

Exploration Strategies and Feature Prioritisation in Contour-based Haptic Perception of 2D Shape.

IEEE transactions on haptics·2026
Same author

Open science practices in behavioral addictions: An exploratory survey.

Journal of behavioral addictions·2026
Same author

[Use of continuous passive motion in inpatient rehabilitation after shoulder replacement-a retrospective study].

Orthopadie (Heidelberg, Germany)·2026
Same author

Hoffa-Kastert Syndrome: A Rare Cause of Acute Knee Blockade.

Indian journal of orthopaedics·2025
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: Apr 27, 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

11.0K

Learning modular policies for robotics.

Gerhard Neumann1, Christian Daniel1, Alexandros Paraschos1

  • 1Department of Computer Science, Intelligent Autonomous Systems, Technische Universität Darmstadt Darmstadt, Germany.

Frontiers in Computational Neuroscience
|June 27, 2014
PubMed
Summary
This summary is machine-generated.

This study introduces a unified approach for robot learning, enabling algorithms to select, adapt, and sequence elemental behaviors for complex tasks. Experiments in simulation and on real robots demonstrate the effectiveness of this modular control architecture.

Keywords:
hierarchical reinforcement learningmodularitymotor controlmovement primitivespolicy searchrobotics

More Related Videos

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

15.0K
Investigating Motor Skill Learning Processes with a Robotic Manipulandum
07:52

Investigating Motor Skill Learning Processes with a Robotic Manipulandum

Published on: February 12, 2017

8.3K

Related Experiment Videos

Last Updated: Apr 27, 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

11.0K
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

15.0K
Investigating Motor Skill Learning Processes with a Robotic Manipulandum
07:52

Investigating Motor Skill Learning Processes with a Robotic Manipulandum

Published on: February 12, 2017

8.3K

Area of Science:

  • Robotics
  • Machine Learning
  • Artificial Intelligence

Background:

  • Scaling robot learning to complex tasks requires composing elemental behaviors.
  • Existing methods lack a unified framework for selecting, adapting, sequencing, and co-activating behaviors.

Purpose of the Study:

  • To present a unified approach for learning modular control architectures in robotics.
  • To develop algorithms that can manage elemental behaviors for complex task execution.

Main Methods:

  • Introduced novel policy search algorithms based on information-theoretic principles.
  • Developed a new representation for elemental behaviors supporting co-activation and adaptation.
  • Conducted experiments in simulation and with real robots.

Main Results:

  • The proposed algorithms effectively learn to select, adapt, and sequence elemental behaviors.
  • The new representation facilitates principled adaptation and co-activation of movement building blocks.
  • Demonstrated successful learning of modular control architectures.

Conclusions:

  • A unified framework for learning modular robot control architectures has been developed.
  • The approach enables robots to learn complex behaviors by composing elemental ones.
  • The method shows promise for advancing robot learning capabilities.