Robotic grasping recognition using multi-modal deep extreme learning machine

Jie, Wei; Liu, Huaping; Yan, Gaowei; Sun, Fuchun

doi:10.1007/s11045-016-0389-0

Cited by 37 publications

(27 citation statements)

References 29 publications

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“…This method yielded a detection accuracy of 75% and a processing time of 13.5 s per image. Similar results were reported from the studies by Wang et al [47] and Wei et al [48] who followed a similar approach. Guo et al [35] used the reference rectangle method to identify graspable regions of an image.…”

supporting

confidence: 90%

Review of Deep Learning Methods in Robotic Grasp Detection

Caldera

Rassau

Chai

2018

MTI

172

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For robots to attain more general-purpose utility, grasping is a necessary skill to master. Such general-purpose robots may use their perception abilities to visually identify grasps for a given object. A grasp describes how a robotic end-effector can be arranged to securely grab an object and successfully lift it without slippage. Traditionally, grasp detection requires expert human knowledge to analytically form the task-specific algorithm, but this is an arduous and time-consuming approach. During the last five years, deep learning methods have enabled significant advancements in robotic vision, natural language processing, and automated driving applications. The successful results of these methods have driven robotics researchers to explore the use of deep learning methods in task-generalised robotic applications. This paper reviews the current state-of-the-art in regards to the application of deep learning methods to generalised robotic grasping and discusses how each element of the deep learning approach has improved the overall performance of robotic grasp detection. Several of the most promising approaches are evaluated and the most suitable for real-time grasp detection is identified as the one-shot detection method. The availability of suitable volumes of appropriate training data is identified as a major obstacle for effective utilisation of the deep learning approaches, and the use of transfer learning techniques is proposed as a potential mechanism to address this. Finally, current trends in the field and future potential research directions are discussed.

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supporting

confidence: 90%

Review of Deep Learning Methods in Robotic Grasp Detection

Caldera

Rassau

Chai

2018

MTI

172

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“…Tang et al () proposed a sparse ELM‐AE, which is an improved version of the existing ELM‐AE, by learning the hidden layer weights sparsely (Tang et al, ). Wei, Liu, Yan, and Sun () proposed a multi‐modal deep ELM‐AE (MM‐DELM) framework, an extended version of DELM, for multi‐modal data analysis and classification. MM‐DELM reportedly had successful performance in robotic grasping problems (Wei et al, ).…”

Section: Introductionmentioning

confidence: 99%

“…Wei, Liu, Yan, and Sun () proposed a multi‐modal deep ELM‐AE (MM‐DELM) framework, an extended version of DELM, for multi‐modal data analysis and classification. MM‐DELM reportedly had successful performance in robotic grasping problems (Wei et al, ).…”

Section: Introductionmentioning

confidence: 99%

Identification of Alzheimer's disease and mild cognitive impairment using multimodal sparse hierarchical extreme learning machine

Kim

Lee

2018

Human Brain Mapping

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Different modalities such as structural MRI, FDG-PET, and CSF have complementary information, which is likely to be very useful for diagnosis of AD and MCI. Therefore, it is possible to develop a more effective and accurate AD/MCI automatic diagnosis method by integrating complementary information of different modalities. In this paper, we propose multi-modal sparse hierarchical extreme leaning machine (MSH-ELM). We used volume and mean intensity extracted from 93 regions of interest (ROIs) as features of MRI and FDG-PET, respectively, and used p-tau, t-tau, and Aβ42 as CSF features. In detail, high-level representation was individually extracted from each of MRI, FDG-PET, and CSF using a stacked sparse extreme learning machine auto-encoder (sELM-AE). Then, another stacked sELM-AE was devised to acquire a joint hierarchical feature representation by fusing the high-level representations obtained from each modality. Finally, we classified joint hierarchical feature representation using a kernel-based extreme learning machine (KELM). The results of MSH-ELM were compared with those of conventional ELM, single kernel support vector machine (SK-SVM), multiple kernel support vector machine (MK-SVM) and stacked auto-encoder (SAE). Performance was evaluated through 10-fold cross-validation. In the classification of AD vs. HC and MCI vs. HC problem, the proposed MSH-ELM method showed mean balanced accuracies of 96.10% and 86.46%, respectively, which is much better than those of competing methods. In summary, the proposed algorithm exhibits consistently better performance than SK-SVM, ELM, MK-SVM and SAE in the two binary classification problems (AD vs. HC and MCI vs. HC).

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“…To improve the generalization ability of network and make the results more stable, we add a regularization term to the [26]. When the number of hidden layer neurons is less than the number of training samples, can be expressed as…”

Section: Extreme Learning Machinementioning

confidence: 99%

A Multiple Hidden Layers Extreme Learning Machine Method and Its Application

Xiao

Mao

2017

Mathematical Problems in Engineering

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Extreme learning machine (ELM) is a rapid learning algorithm of the single-hidden-layer feedforward neural network, which randomly initializes the weights between the input layer and the hidden layer and the bias of hidden layer neurons and finally uses the least-squares method to calculate the weights between the hidden layer and the output layer. This paper proposes a multiple hidden layers ELM (MELM for short) which inherits the characteristics of parameters of the first hidden layer. The parameters of the remaining hidden layers are obtained by introducing a method (make the actual output zero error approach the expected hidden layer output). Based on the MELM algorithm, many experiments on regression and classification show that the MELM can achieve the satisfactory results based on average precision and good generalization performance compared to the two-hidden-layer ELM (TELM), the ELM, and some other multilayer ELM.

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Robotic grasping recognition using multi-modal deep extreme learning machine

Cited by 37 publications

References 29 publications

Review of Deep Learning Methods in Robotic Grasp Detection

Review of Deep Learning Methods in Robotic Grasp Detection

Identification of Alzheimer's disease and mild cognitive impairment using multimodal sparse hierarchical extreme learning machine

A Multiple Hidden Layers Extreme Learning Machine Method and Its Application

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