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

Naturalistic Observations02:30

Naturalistic Observations

16.9K
If you want to understand how behavior occurs, one of the best ways to gain information is to simply observe the behavior in its natural context. However, people might change their behavior in unexpected ways if they know they are being observed. How do researchers obtain accurate information when people tend to hide their natural behavior? As an example, imagine that your professor asks everyone in your class to raise their hand if they always wash their hands after using the restroom. Chances...
16.9K
One-Degree-of-Freedom System01:24

One-Degree-of-Freedom System

761
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...
761
Relative Motion Analysis using Rotating Axes-Problem Solving01:29

Relative Motion Analysis using Rotating Axes-Problem Solving

675
Consider a crane whose telescopic boom rotates with an angular velocity of 0.04 rad/s and angular acceleration of 0.02 rad/s2. Along with the rotation, the boom also extends linearly with a uniform speed of 5 m/s. The extension of the boom is measured at point D, which is measured with respect to the fixed point C on the other end of the boom. For the given instant, the distance between points C and D is 60 meters.
Here, in order to determine the magnitude of velocity and acceleration for point...
675
Observational Learning01:12

Observational Learning

782
Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
782
Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

466
A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
466
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

371
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence of...
371

You might also read

Related Articles

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

Sort by
Same author

Prospective Environmental Impact Assessment of Scaling Up Perovskite/Silicon Tandem Solar Cells to Industrial Applications.

ChemSusChem·2026
Same author

To reuse or not to reuse: That is the question. Environmental trade-offs between reusable and recyclable packaging for large appliances.

Waste management (New York, N.Y.)·2026
Same author

The importance of identifying red flags in patients with elbow pain: A systematic review.

Journal of hand therapy : official journal of the American Society of Hand Therapists·2026
Same author

e T 2.0: An efficient open-source molecular electronic structure program.

The Journal of chemical physics·2026
Same author

Complementary functionalities of extracellular polymeric substances, adhesion ability and hydrophobicity in Pseudomonas isolates may help the selection of strategically advantageous microbial inoculants.

World journal of microbiology & biotechnology·2026
Same author

Convex Hartree-Fock theory for modeling ground state conical intersections.

Communications chemistry·2026
Same journal

Distributed spatial awareness for robot swarms.

Autonomous robots·2025
Same journal

Planning under uncertainty for safe robot exploration using Gaussian process prediction.

Autonomous robots·2024
Same journal

That was not what I was aiming at! Differentiating human intent and outcome in a physically dynamic throwing task.

Autonomous robots·2022
Same journal

Hierarchical planning with state abstractions for temporal task specifications.

Autonomous robots·2022
Same journal

AlphaPilot: autonomous drone racing.

Autonomous robots·2022
Same journal

A new meta-module design for efficient reconfiguration of modular robots.

Autonomous robots·2021
See all related articles

Related Experiment Video

Updated: Jan 7, 2026

Simulation of a Scaled Assembly Process with Collaboration of a Robotic Arm and Monitoring through a Vision System for Quality Control
05:47

Simulation of a Scaled Assembly Process with Collaboration of a Robotic Arm and Monitoring through a Vision System for Quality Control

Published on: August 29, 2025

362

Planned synchronization for multi-robot systems with active observations.

Patrick Zhong1, Federico Rossi2, Dylan A Shell1

  • 1Texas A&M University, College Station, TX 77840 USA.

Autonomous Robots
|December 29, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a novel Markov decision process (MDP) approach for multi-agent robotic systems to plan perception and communication acts efficiently. The method optimizes joint state estimation under uncertainty, enabling effective decentralized execution for cooperative tasks.

Keywords:
Active joint-perceptionCooperative plansPlanned communicationRe-scheduling under uncertainty

More Related Videos

Robotic Sensing and Stimuli Provision for Guided Plant Growth
08:02

Robotic Sensing and Stimuli Provision for Guided Plant Growth

Published on: July 1, 2019

8.5K
Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

10.7K

Related Experiment Videos

Last Updated: Jan 7, 2026

Simulation of a Scaled Assembly Process with Collaboration of a Robotic Arm and Monitoring through a Vision System for Quality Control
05:47

Simulation of a Scaled Assembly Process with Collaboration of a Robotic Arm and Monitoring through a Vision System for Quality Control

Published on: August 29, 2025

362
Robotic Sensing and Stimuli Provision for Guided Plant Growth
08:02

Robotic Sensing and Stimuli Provision for Guided Plant Growth

Published on: July 1, 2019

8.5K
Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

10.7K

Area of Science:

  • Robotics and Artificial Intelligence
  • Multi-Agent Systems
  • Planning Under Uncertainty

Background:

  • Cooperative multi-agent robotic systems require joint action planning based on shared state observations.
  • Planning perception and communication acts under uncertainty is crucial for efficient cooperation.
  • Existing methods struggle with the intractability of large joint belief spaces.

Purpose of the Study:

  • To develop a computationally tractable approach for multi-agent planning under uncertainty.
  • To enable robots to decide proactively when the cost of obtaining state information is justified.
  • To formulate a method suitable for high-quality observations that recover joint states, even if infrequently.

Main Methods:

  • Formulation of the problem as a Markov decision process (MDP) solved over macro-actions.
  • Development of a Bellman-like recurrence to guide policy generation.
  • Policies simultaneously define low-level actions, state recovery stages, and future rescheduling commitments.

Main Results:

  • The proposed MDP formulation effectively sidesteps the need for full joint belief space construction.
  • Demonstrated multi-agency in practical forms: assistance, sensor fusion, and coordinated activity.
  • Successful simulation studies and physical robot implementation validated the approach for decentralized execution.

Conclusions:

  • The developed method provides an effective framework for decentralized execution of joint plans in multi-agent robotic systems.
  • The approach is adaptable to real-world non-idealities, as shown by enhancements based on hardware experience.
  • This work advances cooperative robotic capabilities in scenarios requiring joint state estimation and action planning.