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

Manipulation and Analysis

Manipulation and Analysis

GIS manipulation and analysis functions are vital for decision-making and planning. These activities range from data retrieval tasks, such as selecting information based on specific criteria, to advanced analytical techniques that address complex spatial problems.One critical GIS analysis method is overlaying, which combines multiple data layers to examine impacts. For example, overlaying a river-dammed lake boundary with road networks can identify affected infrastructure. Another common...

Extraction: Advanced Methods

Extraction: Advanced Methods

Metal ions can be separated from one another by complexation with organic ligands–the chelating agent– to form uncharged chelates. Here, the chelating agent must contain hydrophobic groups and behave as a weak acid, losing a proton to bind with the metal. Since most organic ligands used in this process are insoluble or undergo oxidation in the aqueous phase, the chelating agent is initially added to the organic phase and extracted into the aqueous phase. The metal-ligand complex is...

You might also read

Related Articles

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

Sort by

Same author

Vaginal microbiota and genitourinary syndrome of menopause in premenopausal breast cancer patients receiving endocrine therapy: a longitudinal cohort study protocol.

Frontiers in medicine·2026

Same author

PF-CMNet: Progressive Frequency-Aware Cross-Modal Network with Missing-Modality Distillation for 3D Brain Tumor Segmentation.

Brain sciences·2026

Same author

2s-DAS: Two-Stream Diffusion with Multi-Modal Fusion for Temporal Action Segmentation.

Journal of imaging·2026

Same author

Intravenous administration of an engineered AAV9-gene-silencing vector suppresses human SOD1 and extends survival in an ALS mouse model.

Nature communications·2026

Same author

Lamellar Regulation for Fast and Reversible Zinc-Ion Transport in Water-Rich Hydrogels for Aqueous Zinc-Ion Batteries.

Small (Weinheim an der Bergstrasse, Germany)·2026

Same author

Application of large language models in medical diagnosis: A bibliometric review.

Digital health·2026

Same journal

Passive wheels on legged robots: a survey.

Frontiers in robotics and AI·2026

Same journal

Politeness cannot make up for robots' errors.

Frontiers in robotics and AI·2026

Same journal

Workers expect basic social skills but limited autonomy from future robots - a qualitative interview study and taxonomy for robot social skills.

Frontiers in robotics and AI·2026

Same journal

Human-robot interaction in sustainable hospitality: how robot type shapes customer emotions, green perceptions, and service loyalty.

Frontiers in robotics and AI·2026

Same journal

Dynamic variance-aware federated tuning for efficient autonomous vehicle perception under non-IID settings.

Frontiers in robotics and AI·2026

Same journal

Editorial: Synergizing large language models and computational intelligence for advanced robotic systems.

Frontiers in robotics and AI·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Sep 28, 2025

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

DeepClaw 2.0: A Data Collection Platform for Learning Human Manipulation.

Haokun Wang¹, Xiaobo Liu², Nuofan Qiu²

¹Robotics and Autonomous Systems Thrust, System Hub, Hong Kong University of Science and Technology, Hong Kong, Hong Kong SAR, China.

Frontiers in Robotics and AI

|April 4, 2022

Summary

This summary is machine-generated.

DeepClaw 2.0 is a new, affordable, open-source platform for collecting human manipulation data. It uses soft finger networks and an RGB-D camera to capture tactile information for robot learning.

Keywords:

data collection imitation learning robot learning soft robotics vis-tac sensing

More Related Videos

Automated Rat Single-Pellet Reaching with 3-Dimensional Reconstruction of Paw and Digit Trajectories

Automated Rat Single-Pellet Reaching with 3-Dimensional Reconstruction of Paw and Digit Trajectories

Published on: July 10, 2019

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

Related Experiment Videos

Last Updated: Sep 28, 2025

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

Automated Rat Single-Pellet Reaching with 3-Dimensional Reconstruction of Paw and Digit Trajectories

Automated Rat Single-Pellet Reaching with 3-Dimensional Reconstruction of Paw and Digit Trajectories

Published on: July 10, 2019

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

Area of Science:

Robotics
Human-Computer Interaction
Machine Learning

Background:

Human hands excel at object manipulation using tools.
Robots often struggle with the structural differences compared to human hands.
Learning human manipulation skills is crucial for advanced robotics.

Purpose of the Study:

Introduce DeepClaw 2.0, a low-cost, open-source data collection platform.
Facilitate learning human manipulation for robots.
Bridge the structural gap between human and robotic manipulation.

Main Methods:

Utilized an RGB-D camera to track soft finger network motion and deformation.
Employed a modified kitchen tong operated by human demonstrators.
Developed an interface to convert sensor data into state-action data for imitation learning.

Main Results:

Collected a comprehensive dataset with five demonstrators across ten manipulation tasks.
Captured passive tactile information through soft finger deformation.
Demonstrated the dataset's utility for learning-by-demonstration using real robotic hardware and simulation.

Conclusions:

DeepClaw 2.0 provides a valuable resource for robotic manipulation research.
The platform enables efficient data collection for imitation learning.
Soft finger networks offer a promising approach for capturing tactile feedback in robotics.