Title

Robotic grasp pose detection using deep learning

Document Type

Conference Proceeding

Publisher

Institute of Electrical and Electronics Engineers Inc.

School

School of Engineering

Comments

Originally published as: Caldera, S., Rassau, A., & Chai, D. (2018, November). Robotic Grasp Pose Detection Using Deep Learning. In 2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV) (pp. 1966-1972). IEEE. Original paper available here

Abstract

Recent advancements in Deep Learning have accelerated the capabilities of robotic systems in terms of visual perception, object manipulation, automated navigation, and human-robot collaboration. This paper proposes the use of a transfer learning technique with deep convolutional neural networks to learn how to visually identify the grasping configurations for a parallel plate gripper that will be used to grasp various household objects. The Red-Green-Blue-Depth (RGB-D) data from the Cornell Grasp Dataset is used to train the network model using an end-to-end learning method. With this method, we achieve a grasping configuration prediction accuracy of 93.91%.

DOI

10.1109/ICARCV.2018.8581091

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