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Related Experiment Video

Updated: Jan 1, 2026

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

A Model-Based Recurrent Neural Network With Randomness for Efficient Control With Applications.

Yangming Li¹, Shuai Li², Blake Hannaford³

¹College of Engineering Technology, Rochester Institute of Technology, Rochester, NY, USA 14623. The major part of this work was done when he was with the BioRobotics Lab at University of Washington, Seattle, WA, USA 98195.

IEEE Transactions on Industrial Informatics

|December 31, 2019

Summary

This summary is machine-generated.

This study introduces a novel random Recurrent Neural Network (RNN) control scheme for redundant manipulators, enhancing planning completeness and efficiency. The new method improves control precision and robustness, overcoming limitations of traditional RNN approaches.

Keywords:

Motion Planning Random Neural Networks Recurrent Neural Networks Redundant Manipulator Robot

Related Experiment Videos

Last Updated: Jan 1, 2026

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

Area of Science:

Robotics
Artificial Intelligence
Control Systems

Background:

Recurrent Neural Network (RNN) control schemes offer computational efficiency, precision, and robustness for redundant manipulators.
A key limitation of current RNN control schemes is the lack of planning completeness.

Purpose of the Study:

To analyze the reasons behind the planning incompleteness in existing RNN control schemes.
To propose a novel random RNN control scheme that addresses planning completeness and enhances performance.

Main Methods:

Introducing randomness into the RNN control scheme to achieve planning completeness.
Implementing a new optimization target to improve control precision.
Utilizing learning from exploration to enhance planning efficiency.
Conducting theoretical analyses to validate global stability, planning completeness, and computational complexity.

Main Results:

The proposed random RNN control scheme demonstrates improved robustness against noise compared to benchmark methods.
Software simulations confirm enhanced planning completeness and efficiency.
Real-world experiments validate the practical applicability of the new control scheme.

Conclusions:

The novel random RNN control scheme effectively overcomes the planning completeness limitations of traditional RNNs.
The method offers significant improvements in control precision, planning efficiency, and robustness for redundant manipulators.
This work provides a theoretically sound and experimentally validated approach for advanced robotic control.