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

Design Example: Resistive Touchscreen01:14

Design Example: Resistive Touchscreen

649
A device engineer plays a crucial role in designing user interfaces for mobile devices. One such interface is the resistive touchscreen, which fundamentally consists of two metallic layers: a flexible upper layer and a rigid lower layer, separated by a narrow gap. The high resistance between these two layers is a key characteristic of this design.
When a user touches the screen, the two layers make contact at a specific point known as the touchpoint. This contact reduces the resistance between...
649
Effects of feedback01:24

Effects of feedback

899
Feedback in control systems plays a critical role in shaping various operational parameters, extending beyond simple error reduction to influence stability, bandwidth, gain, impedance, and sensitivity. Understanding these effects requires examining a basic feedback system characterized by defined input, output, error, and feedback signals.
Feedback significantly modifies the gain of a control system. The gain of a system without feedback is altered by a factor of one plus GH, where G represents...
899
Feedback control systems01:26

Feedback control systems

634
Feedback control systems are categorized in various ways based on their design, analysis, and signal types.
Linear feedback systems are theoretical models that simplify analysis and design. These systems operate under the principle that their output is directly proportional to their input within certain ranges. For instance, an amplifier in a control system behaves linearly as long as the input signal remains within a specific range. However, most physical systems exhibit inherent nonlinearity...
634

You might also read

Related Articles

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

Sort by
Same author

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

Experimental brain research·2026
Same author

Two Cases of Neuroendocrine Transformation of Prostate Carcinoma in the Era of Expanding Therapeutic Options: Do the Increased Number of Available Treatments Translate Into Improved Patient Outcomes?

Cureus·2026
Same author

Models of spiritual intelligence interventions: A scoping review.

Nurse education in practice·2023
Same author

Hierarchical Tactile-Based Control Decomposition of Dexterous In-Hand Manipulation Tasks.

Frontiers in robotics and AI·2021
Same author

Entropic Regularization of Markov Decision Processes.

Entropy (Basel, Switzerland)·2020
Same author

Grip Stabilization of Novel Objects Using Slip Prediction.

IEEE transactions on haptics·2018
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
See all related articles

Related Experiment Video

Updated: Dec 25, 2025

Measurement of Spatial Stability in Precision Grip
09:36

Measurement of Spatial Stability in Precision Grip

Published on: June 4, 2020

3.5K

Grip Stabilization through Independent Finger Tactile Feedback Control.

Filipe Veiga1, Benoni Edin2, Jan Peters3,4

  • 1Computer Science and Artificial Intelligence Laboratory (CSAIL), Massachusetts Institute of Technology, Cambridge, MA 02139, USA.

Sensors (Basel, Switzerland)
|April 5, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a modular grip stabilization method for robotic hands, inspired by human grasp stability. Independent controllers predict and prevent local finger slip using tactile signals, enabling stable manipulation of diverse objects without central coordination.

Keywords:
in-hand manipulationindependent finger controlmodular controlreactive controlslip predictiontactile feedback

More Related Videos

Force and Position Control in Humans - The Role of Augmented Feedback
06:31

Force and Position Control in Humans - The Role of Augmented Feedback

Published on: June 19, 2016

8.1K
Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping
09:41

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping

Published on: April 21, 2023

2.1K

Related Experiment Videos

Last Updated: Dec 25, 2025

Measurement of Spatial Stability in Precision Grip
09:36

Measurement of Spatial Stability in Precision Grip

Published on: June 4, 2020

3.5K
Force and Position Control in Humans - The Role of Augmented Feedback
06:31

Force and Position Control in Humans - The Role of Augmented Feedback

Published on: June 19, 2016

8.1K
Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping
09:41

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping

Published on: April 21, 2023

2.1K

Area of Science:

  • Robotics
  • Biomimetics
  • Control Systems

Background:

  • Current robotic in-hand manipulation relies on monolithic controllers, requiring object and contact models.
  • These monolithic approaches exhibit poor generalization to new manipulation tasks.
  • Human grasp stability mechanisms are not fully replicated in current robotic systems.

Purpose of the Study:

  • To propose a modular grip stabilization method for robotic hands.
  • To develop a biomimetic approach inspired by human grasp stability.
  • To enable stable robotic manipulation without complex central control.

Main Methods:

  • Implemented independent tactile grip stabilization controllers for each robot finger.
  • Utilized tactile signals from fingertip sensors (BioTac and BioTac SP) to predict local slip.
  • Engaged controllers in multi-digit robotic hands without central communication.

Main Results:

  • Stable grasps emerged from independent local slip prevention controllers.
  • The modular method demonstrated resistance to external perturbations.
  • The system achieved stable grips on a wide variety of objects.

Conclusions:

  • A modular, biomimetic approach can achieve stable robotic grip control.
  • Independent tactile feedback and local slip prevention are key to robust manipulation.
  • This method offers a generalized solution for robotic in-hand manipulation.