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An improved kernel based extreme learning machine for robot execution failures.

Bin Li¹, Xuewen Rong², Yibin Li²

¹School of Control Science and Engineering, Shandong University, Jinan, Shandong 250061, China ; School of Science, Qilu University of Technology, Jinan, Shandong 250353, China.

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Summary

Predicting robot execution failures is challenging with limited, imperfect data. A new algorithm, AKELM, optimizes kernel parameters for improved classification accuracy in robot tasks.

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Area of Science:

Robotics
Machine Learning
Artificial Intelligence

Background:

Robot execution failure prediction is a complex machine learning problem.
Partially corrupted, incomplete, or erroneous data and limited samples hinder accurate prediction.
Existing prediction techniques are often unsuitable for this specific challenge.

Purpose of the Study:

To enhance the accuracy of robot execution failure prediction.
To address the challenges posed by limited and imperfect data in robot task analysis.

Main Methods:

Proposal of a novel Adaptive Kernel Extreme Learning Machine (AKELM) learning algorithm.
Utilizing particle swarm optimization to optimize the kernel function parameters of neural networks.
Testing the AKELM algorithm on robot execution failure datasets and benchmark problems.

Main Results:

The AKELM algorithm demonstrated superior generalization performance compared to standard KELM and other methods.
Optimized kernel parameters significantly improved classification accuracy in robot failure prediction.
Simulations confirmed the efficiency and effectiveness of the AKELM approach on various datasets.

Conclusions:

The proposed AKELM algorithm effectively improves robot execution failure prediction accuracy, even with limited or erroneous data.
AKELM offers a robust solution for a critical challenge in the field of robotics.
The optimization of kernel parameters is key to the algorithm's enhanced performance.