Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Uncertainty in Measurement: Reading Instruments

Uncertainty in Measurement: Reading Instruments

Counting is the type of measurement that is free from uncertainty, provided the number of objects being counted does not change during the process. Such measurements result in exact numbers. By counting the eggs in a carton, for instance, one can determine exactly how many eggs are there in the carton. Similarly, the numbers of defined quantities are also exact. For example, 1 foot is exactly 12 inches, 1 inch is exactly 2.54 centimeters, and 1 gram is exactly 0.001 kilograms. Quantities...

Estimation of the Physical Quantities

Estimation of the Physical Quantities

On many occasions, physicists, other scientists, and engineers need to make estimates of a particular quantity. These are sometimes referred to as guesstimates, order-of-magnitude approximations, back-of-the-envelope calculations, or Fermi calculations. The physicist Enrico Fermi was famous for his ability to estimate various kinds of data with surprising precision. Estimating does not mean guessing a number or a formula at random. Instead, estimation means using prior experience and sound...

You might also read

Related Articles

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

Sort by

Same author

Lysozyme-Associated Nephropathy in Myeloid Neoplasms: A Clinicopathological and Mass Spectrometric Study of Two Cases.

Nephron·2026

Same author

Synergy of postural adaptation and exteroception for robust CPG-driven quadrupedal locomotion.

Scientific reports·2026

Same author

Unified three-dimensional bipedal locomotion control via ground reaction force-based joint compliance modulation.

Journal of the Royal Society, Interface·2026

Same author

Human-inspired bipedal locomotion: from neuromechanics to mathematical modelling and robotic applications.

Journal of the Royal Society, Interface·2026

Same author

Deep learning-based robotic cloth manipulation applications: systematic review, challenges and opportunities for physical AI.

Frontiers in robotics and AI·2026

Same author

Deep Learning-Based Decoding and Feature Visualization of Motor Imagery Speeds From EEG Signals.

IEEE open journal of engineering in medicine and biology·2026

Same journal

DSPE-ViT: a lightweight vision transformer with dynamic sparse positional encoding for dense small object detection in UAV imagery.

Frontiers in neurorobotics·2026

Same journal

ST-HONet: Spatio-Temporal Hierarchical Network for long-horizon bimanual visuomotor imitation.

Frontiers in neurorobotics·2026

Same journal

ST-HADP: Spatio-Temporal hierarchical attention diffusion policy for long-horizon generalizable bimanual visuomotor imitation.

Frontiers in neurorobotics·2026

Same journal

EQISP: efficient quantized image signal processing with multi-scale pyramid fusion for resource constrained embodied perception.

Frontiers in neurorobotics·2026

Same journal

Research on embodied agent multimodal perception and real-time path planning algorithms for complex unstructured environments.

Frontiers in neurorobotics·2026

Same journal

NL-YOLOv5: a model with a larger receptive field and the ability to globally acquire features.

Frontiers in neurorobotics·2026

See all related articles

Search research articles

Related Experiment Video

Updated: May 8, 2026

Measurement of Spatial Stability in Precision Grip

Measurement of Spatial Stability in Precision Grip

Published on: June 4, 2020

Learning-based object's stiffness and shape estimation with confidence level in multi-fingered hand grasping.

Kyo Kutsuzawa¹, Minami Matsumoto¹, Dai Owaki¹

¹Neuro-Robotics Laboratory, Department of Robotics, Graduate School of Engineering, Tohoku University, Sendai, Japan.

Frontiers in Neurorobotics

|December 4, 2024

Summary

This summary is machine-generated.

This study introduces a new method for multi-fingered hands to estimate object stiffness and shape in real-time. The system uses recurrent neural networks to quantify estimation confidence, enhancing robotic manipulation.

Keywords:

deep learning grasping probabilistic inference proprioception robotic hand shape estimation stiffness estimation

More Related Videos

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

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

Published on: April 21, 2023

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision

Published on: March 28, 2025

Related Experiment Videos

Last Updated: May 8, 2026

Measurement of Spatial Stability in Precision Grip

Measurement of Spatial Stability in Precision Grip

Published on: June 4, 2020

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

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

Published on: April 21, 2023

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision

Published on: March 28, 2025

Area of Science:

Robotics
Machine Learning
Human-Computer Interaction

Background:

Humans intuitively estimate object properties like stiffness and shape via touch.
Assessing confidence in these estimations is crucial for dexterous manipulation.
Current robotic systems often lack robust methods for real-time property estimation and confidence assessment.

Purpose of the Study:

To develop a method for multi-fingered robotic hands to estimate object stiffness and shape.
To measure the confidence levels associated with these estimations using proprioceptive signals.
To implement a learning framework for real-time, uncertainty-aware property estimation.

Main Methods:

Developed a probabilistic inference-based learning framework.
Implemented recurrent neural networks (RNNs) for real-time estimation.
Utilized proprioceptive signals (joint angles, velocity) for confidence measurement.

Main Results:

Trained neural networks accurately estimate object stiffness and shape.
The system quantifies estimation confidence through variance and entropy.
Demonstrated the ability to represent uncertainty and task difficulty.

Conclusions:

The proposed approach enables reliable state estimation for robotic hands.
This method can be integrated with flexible object manipulation strategies.
Facilitates probabilistic inference-based decision-making in robotics.