Genetic Algorithm-Based Ensemble Hybrid Sparse ELM for Grasp Stability Recognition With Multimodal Tactile Signals

Grasp stability recognition based on tactile perception has attracted increasing attention in the robotics community. In this article, we extract tactile features from multimodal tactile signals and propose a novel ensemble approach named genetic algorithm-based ensemble hybrid sparse extreme learni...

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Bibliographic Details
Published in:IEEE transactions on industrial electronics (1982) 2023-03, Vol.70 (3), p.2790-2799
Main Authors: Yi, Zhengkun, Xu, Tiantian, Shang, Wanfeng, Li, Weimin, Wu, Xinyu
Format: Article
Language:English
Subjects:
Artificial neural networks
Extreme learning machine (ELM)
Extreme learning machines
Feature extraction
Genetic algorithms
grasp stability recognition
large-margin supervised learning
Machine learning
Optimization
Recognition
Robotics
Robots
Sensors
Sparsity
Stability
Stability criteria
Tactile discrimination
tactile perception
Task analysis
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Summary:Grasp stability recognition based on tactile perception has attracted increasing attention in the robotics community. In this article, we extract tactile features from multimodal tactile signals and propose a novel ensemble approach named genetic algorithm-based ensemble hybrid sparse extreme learning machine (GA-EHSELM) for the grasp stability recognition task. In contrast to traditional ensemble extreme learning machines (ELMs), the proposed approach takes the sparsity of the random weights assigned to the connection between the input layer and the hidden layer for each base learner into consideration, in order to avoid the problem of overfitting. In addition, The diversity of base learners is increased by constructing two types of sparse ELM (SELM). The random weights of the base SELMs are sampled from two different distributions with a certain probability. Furthermore, we utilize the genetic algorithm (GA) for the optimization of tactile features and the base sparse ELMs. To be specific, GA is employed for feature selection as well as the optimization of the sparsity of the random weights associated with different types of base learners and the number of each type of base learner. The effectiveness of the proposed approach has been demonstrated on a public tactile grasp stability dataset.
ISSN:0278-0046
1557-9948
DOI:10.1109/TIE.2022.3170631