Preprint Review Version 1 This version is not peer-reviewed

Review of Deep Learning Methods in Robotic Grasp Detection

Version 1 : Received: 31 May 2018 / Approved: 31 May 2018 / Online: 31 May 2018 (17:27:23 CEST)

A peer-reviewed article of this Preprint also exists.

Caldera, S.; Rassau, A.; Chai, D. Review of Deep Learning Methods in Robotic Grasp Detection. Multimodal Technologies Interact. 2018, 2, 57. Caldera, S.; Rassau, A.; Chai, D. Review of Deep Learning Methods in Robotic Grasp Detection. Multimodal Technologies Interact. 2018, 2, 57.

Journal reference: Multimodal Technologies Interact. 2018, 2, 57
DOI: 10.3390/mti2030057

Abstract

In order for robots to attain more general-purpose utility, grasping is a necessary skill to master. Such general-purpose robots may use their perception abilities in order to visually identify grasps for a given object. A grasp describes how a robotic end-effector can be arranged on top of an object to securely grab it between the robotic gripper 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 application 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. A number of the most promising approaches are evaluated and the most successful for 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 identified as a potential mechanism to address this. Finally, current trends in the field and future potential research directions are discussed.

Subject Areas

deep learning; deep convolutional neural networks; dcnn; convolutional neural networks; cnn; robot learning; transfer learning; robotic grasping; robotic grasp detection; human-robot collaboration

Comments (2)

Comment 1
Received: 4 June 2018
Commenter: Khurram Hameed
The commenter has declared there is no conflict of interests.
Comment: A concise and complete detail of robotic grasp has been presented in the paper. The paper presents a detail on the grasping window for grasping the daily-life objects by a robot.
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Response 1 to Comment 1
Received: 4 June 2018
Commenter: Shehan Caldera
The commenter has declared there is no conflict of interests.
Comment: Hi Khurram, thanks very much for the comment.

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