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Updated: May 5, 2026

Investigating Motor Skill Learning Processes with a Robotic Manipulandum

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Published on: February 12, 2017

Task-Oriented Tool Manipulation With Robotic Dexterous Hands: A Knowledge Graph Approach From Fingers to

Fan Yang, Wenrui Chen, Haoran Lin

IEEE Transactions on Cybernetics

|March 3, 2025

Summary

This study introduces a novel semantic knowledge-driven approach for robotic tool manipulation, enhancing finger function allocation. The finger-to-function knowledge graph and adaptive force feedback improve precision and success rates in robotic grasping.

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Published on: April 21, 2023

Area of Science:

Robotics
Artificial Intelligence
Human-Computer Interaction

Background:

Dexterous robotic hands require precise manipulation for human-like tool use.
Existing research often overlooks functional finger allocation during object interaction.
Achieving fine motor control necessitates understanding human tool use strategies.

Purpose of the Study:

To develop a semantic knowledge-driven framework for distributing finger functions in robotic tool manipulation.
To enhance the precision and efficiency of robotic hand-object interactions.
To enable robots to mimic human-like dexterity in tool use without extensive annotated data.

Main Methods:

Developed a finger-to-function (F2F) knowledge graph to encode human expertise in tool use.
Utilized knowledge graph semantic embedding for a manipulation element-oriented prediction algorithm.
Integrated a functionality-integrated adaptive force feedback manipulation (FAFM) module for precise finger-level control.

Main Results:

The F2F knowledge graph captures relationships between tool attributes, tasks, and manipulation elements.
The prediction algorithm improves the speed and accuracy of manipulation element prediction.
The FAFM module achieves precise finger-level control through adaptive force feedback.
The framework demonstrated superior performance and generalizability in real-world scenarios.

Conclusions:

The proposed semantic knowledge-driven approach effectively guides robotic tool manipulation.
The method achieves an 8% higher success rate in grasping and manipulation compared to state-of-the-art techniques.
This framework offers a data-efficient solution for enhancing robotic dexterity and tool use.