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Characterization of the Sense of Agency over the Actions of Neural-machine Interface-operated Prostheses

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Neural network control of multifingered robot hands using visual feedback.

Yu Zhao¹, Chien Chern Cheah

¹School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore. ZhaoYu@pmail.ntu.edu.sg

IEEE Transactions on Neural Networks

|April 17, 2009

Summary

This summary is machine-generated.

This study introduces an adaptive neural network (NN) controller for robot hands, enabling skillful object manipulation despite unknown finger movements and contact points. The NN controller ensures precise control even with uncertainties.

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Area of Science:

Robotics
Artificial Intelligence
Control Systems

Background:

Human dexterity in object manipulation contrasts with rigid assumptions in current multifingered robot control.
Existing robot control methods often require precise knowledge of finger kinematics and contact points, limiting their applicability.
Real-world multifingered robot hands frequently encounter uncertainties in kinematics, dynamics, and unknown Jacobian matrix structures.

Purpose of the Study:

To develop an adaptive neural network (NN) Jacobian controller for multifingered robot hands.
To address uncertainties in kinematics, Jacobian matrices, and dynamics inherent in robot hand control.
To achieve robust and precise object manipulation in the presence of unknown parameters.

Main Methods:

An adaptive neural network (NN) based Jacobian controller was designed for multifingered robot hands.
The controller was developed to handle uncertainties in robot kinematics, dynamics, and Jacobian matrices.
Simulations were used to validate the controller's performance and stability.

Main Results:

The proposed adaptive NN controller demonstrated the ability to achieve uniform ultimate boundedness of position error.
The controller effectively managed uncertainties in kinematics, Jacobian matrices, and dynamics.
Simulation results confirmed the controller's capability to ensure precise robot hand control.

Conclusions:

Adaptive neural networks provide a robust solution for multifingered robot hand control with uncertainties.
The developed NN Jacobian controller enhances the dexterity and reliability of robot hands in complex tasks.
This approach paves the way for more adaptable and skillful robotic manipulation in unstructured environments.