Review of Deep Learning Methods in Robotic Grasp Detection

• Published in: Multimodal technologies and interaction Vol. 2; no. 3; p. 57
• Main Authors: Caldera, Shehan, Rassau, Alexander, Chai, Douglas
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.09.2018
• Subjects: Artificial intelligence; cnn; convolutional neural networks; dcnn; Decision making; deep convolutional neural networks; Deep learning; Grasping (robotics); human-robot collaboration; Information processing; Kinematics; Natural language processing; Neural networks; Pattern recognition; robot learning; robotic grasp detection; robotic grasping; Robotics; Robots; Slippage; State-of-the-art reviews; transfer learning; New York; United States > US; Las Vegas Nevada
• ISSN: 2414-4088; 2414-4088
• DOI: 10.3390/mti2030057

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.