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

Virtual Work for a System of Connected Rigid Bodies01:06

Virtual Work for a System of Connected Rigid Bodies

471
Virtual work is a powerful method used to solve problems involving several connected rigid bodies. When the system is in equilibrium, virtual work is zero. This allows the calculation of the resulting forces when a system undergoes a virtual displacement. When attempting to analyze such a system, first, use a free-body diagram, where an independent coordinate represents the configuration of the links, and mark its deflected position resulting from the positive virtual displacement.
Next,...
471
Three-Dimensional Force System:Problem Solving01:30

Three-Dimensional Force System:Problem Solving

860
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...
860
Three-Dimensional Force System01:30

Three-Dimensional Force System

2.3K
In mechanical engineering, a three-dimensional force system is a system of forces acting in three dimensions, with forces applied along the x, y, and z coordinate axes. The three-dimensional force system is an important concept in mechanical engineering, as it allows engineers to understand and analyze the behavior of objects and structures in three dimensions. By understanding the forces acting on a system, engineers can design more efficient and effective mechanical systems that can withstand...
2.3K
Mechanical Systems01:22

Mechanical Systems

289
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...
289
One-Degree-of-Freedom System01:24

One-Degree-of-Freedom System

556
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...
556
Two-Dimensional Force System: Problem Solving01:29

Two-Dimensional Force System: Problem Solving

670
Solving problems related to two-dimensional force systems is an essential aspect of mechanics and engineering. By applying the principles of vector analysis and force equilibrium, one can determine the effect of multiple forces acting on an object in a two-dimensional space.
The first step to solving a two-dimensional force system problem is to draw a free-body diagram of the object under consideration. This diagram helps identify all the external forces acting on the object, including their...
670

You might also read

Related Articles

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

Sort by
Same author

Reservoir computing bootcamp-From Python/NumPy tutorial for the complete beginners to cutting-edge research topics of reservoir computing.

Chaos (Woodbury, N.Y.)·2026
Same author

Predicting bifurcation of mechanical systems using reservoir computing: Case studies on legged locomotion and pneumatic soft actuator.

Chaos (Woodbury, N.Y.)·2026
Same author

Quantization induced memory-nonlinearity transfer: Implications of analog-to-digital conversion in reservoir computing.

Chaos (Woodbury, N.Y.)·2025
Same author

Noise and Dynamical Synapses as Optimization Tools for Spiking Neural Networks.

Entropy (Basel, Switzerland)·2025
Same author

Flexible electronic brush: Real-time multimodal sensing powered by reservoir computing through whisker dynamics.

Science advances·2025
Same author

Modeling long-term nutritional behaviors using deep homeostatic reinforcement learning.

PNAS nexus·2024
Same journal

Gap junction architecture and synchronization clusters in the thalamic reticular nuclei.

Chaos (Woodbury, N.Y.)·2026
Same journal

Exact computation of Lyapunov exponents via system parameters in multi-triangle chaotic maps: Bifurcation analysis and circuit realization.

Chaos (Woodbury, N.Y.)·2026
Same journal

Integrating score-based generative modeling and neural ODEs for accurate representation of multiscale chaotic dynamics.

Chaos (Woodbury, N.Y.)·2026
Same journal

A data-driven tuberculosis model with behavioral changes and saturated treatment: Optimal control and cost-effectiveness study.

Chaos (Woodbury, N.Y.)·2026
Same journal

Breathers, rational solutions, and their exact physical spectra in F = 1 spinor Bose-Einstein condensates.

Chaos (Woodbury, N.Y.)·2026
Same journal

Finite invariant sets with bridging points in logistic IFS.

Chaos (Woodbury, N.Y.)·2026
See all related articles

Related Experiment Video

Updated: Sep 12, 2025

Fabrication of Soft Pneumatic Network Actuators with Oblique Chambers
07:09

Fabrication of Soft Pneumatic Network Actuators with Oblique Chambers

Published on: August 17, 2018

9.2K

Multifunctional physical reservoir computing in soft tensegrity robots.

Ryo Terajima1, Katsuma Inoue1, Kohei Nakajima1,2

  • 1School of Information Science and Technology, The University of Tokyo, Tokyo, Japan.

Chaos (Woodbury, N.Y.)
|August 4, 2025
PubMed
Summary
This summary is machine-generated.

Physical reservoir computing (PRC) uses soft robot dynamics for information processing. This study shows tensegrity robots can learn multiple behaviors and reveal intrinsic properties through "untrained attractors" in embodied AI.

More Related Videos

Manufacturing, Control, and Performance Evaluation of a Gecko-Inspired Soft Robot
07:40

Manufacturing, Control, and Performance Evaluation of a Gecko-Inspired Soft Robot

Published on: June 10, 2020

14.8K
Design and Fabrication of an Elastomeric Unit for Soft Modular Robots in Minimally Invasive Surgery
11:06

Design and Fabrication of an Elastomeric Unit for Soft Modular Robots in Minimally Invasive Surgery

Published on: November 14, 2015

9.0K

Related Experiment Videos

Last Updated: Sep 12, 2025

Fabrication of Soft Pneumatic Network Actuators with Oblique Chambers
07:09

Fabrication of Soft Pneumatic Network Actuators with Oblique Chambers

Published on: August 17, 2018

9.2K
Manufacturing, Control, and Performance Evaluation of a Gecko-Inspired Soft Robot
07:40

Manufacturing, Control, and Performance Evaluation of a Gecko-Inspired Soft Robot

Published on: June 10, 2020

14.8K
Design and Fabrication of an Elastomeric Unit for Soft Modular Robots in Minimally Invasive Surgery
11:06

Design and Fabrication of an Elastomeric Unit for Soft Modular Robots in Minimally Invasive Surgery

Published on: November 14, 2015

9.0K

Area of Science:

  • Robotics
  • Artificial Intelligence
  • Computational Neuroscience

Background:

  • Physical reservoir computing (PRC) leverages physical system dynamics for information processing.
  • Soft robots, like tensegrity robots, possess nonlinear dynamics exploitable for computation and motor control.
  • Embodied artificial intelligence seeks to integrate physical systems with intelligent control.

Purpose of the Study:

  • To extend PRC for controlling multiple behaviors in tensegrity robots.
  • To investigate the emergent properties and dynamics of the robot-environment system.
  • To explore the potential of PRC in understanding embodied cognition.

Main Methods:

  • Simulation study of a tensegrity robot interacting with its environment.
  • Application of physical reservoir computing principles for behavior embedding.
  • Attractor analysis of the multistable dynamical system's state space.

Main Results:

  • The tensegrity robot system demonstrated the ability to control and embed multiple behaviors.
  • The robot-environment system exhibited multistable dynamics, converging to different attractors.
  • Discovery of "untrained attractors" reflecting intrinsic robot and environmental properties beyond training data.

Conclusions:

  • PRC offers a novel framework for controlling complex behaviors in soft robots.
  • Untrained attractors provide insights into the inherent capabilities and structure of embodied systems.
  • This approach has significant implications for advancing embodied artificial intelligence and understanding cognition.