Predicting Multiple Pregrasping Poses by Combining Deep Convolutional Neural Networks with Mixture Density Networks
- Title
- Predicting Multiple Pregrasping Poses by Combining Deep Convolutional Neural Networks with Mixture Density Networks
- Author
- 서일홍
- Keywords
- Grasping pose prediction; Deep convolutional neural network; Mixture density network
- Issue Date
- 2016-09
- Publisher
- Springer Nature
- Citation
- Lecture Notes in Computer Science, v. 9949, Page. 581-590
- Abstract
- In this paper, we propose a deep neural network to predict the pregrasp poses of a three-dimensional (3D) object. Specifically, a single RGB-D image is used to determine multiple pregrasp position of three fingers of the robotic hand for various poses of known or unknown objects. Multiple pregrasping pose prediction typically involves the use of complex multi-valued functions where standard regression models fail. To this end, we propose a deep neural network containing a variant of the traditional deep convolutional neural network as well as a mixture density network. Furthermore, in order to overcome the difficulty of learning with insufficient data in the first part of the proposed network, we develop a supervised learning technique to pretrain the variant of the convolutional neural network.
- URI
- https://link.springer.com/chapter/10.1007%2F978-3-319-46675-0_64https://repository.hanyang.ac.kr/handle/20.500.11754/76728
- ISBN
- 978-3-319-46674-3
- ISSN
- 1611-3349; 0302-9743; 978-3-319-46675-0
- DOI
- 10.1007/978-3-319-46675-0_64
- Appears in Collections:
- COLLEGE OF ENGINEERING[S](공과대학) > ELECTRONIC ENGINEERING(융합전자공학부) > Articles
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