Multimodal features deep learning for robotic potential grasp recognition

被引：0
|
作者：
Zhong X.-G. [1 ]
Xu M. [1 ]
Zhong X.-Y. [2 ]
Peng X.-F. [2 ]
机构：
[1] School of Electrical Engineering and Automation, Xiamen University of Technology, Xiamen
[2] Department of Automation, Xiamen University, Xiamen
来源：
Zidonghua Xuebao/Acta Automatica Sinica | 2016年 / 42卷 / 07期
基金：
中国国家自然科学基金;
关键词：
Denoising auto-encoding (DAE); Multimodal features; Robot grasping recognition; Stacked deep learning;
D O I：
10.16383/j.aas.2016.c150661
中图分类号：
学科分类号：
摘要：
In this paper, a multimodal features deep learning and a fusion approach are proposed to address the problem of robotic potential grasp recognition. In our thinking, the test features which diverge from training are presented as noise-processing, then the denoising auto-encoding (DAE) and sparse constraint conditions are introduced to realize the network's weights training. Furthermore, a stacked DAE with fusion method is adopted to deal with the multimodal vision dataset for its high-level abstract expression. These two strategies aim at improving the network's robustness and the precision of grasp recognition. A six-degree-of-freedom robotic manipulator with a stereo camera configuration is used to demonstrate the robotic potential grasp recognition. Experimental results show that the robot can optimally localizate the target by simulating human grasps, and that the proposed method is robust to a variety of new target grasp recognition. Copyright © 2016 Acta Automatica Sinica. All rights reserved.
引用
收藏
页码：1022 / 1029
页数：7
相关论文
共 28 条
  • [11] Ngiam J., Khosla A., Kim M., Nam J., Lee H., Ng A.Y., Multimodal deep learning, Proceedings of the 28th International Conference on Machine Learning, pp. 689-696, (2011)
  • [12] Baldi P., Lu Z.Q., Complex-valued autoencoders, Neural Networks, 33, pp. 136-147, (2012)
  • [13] Wu P.C., Hoi S.C.H., Xia H., Zhao P.L., Wang D.Y., Miao C.Y., Online multimodal deep similarity learning with application to image retrieval, Proceedings of the 21st ACM International Conference on Multimedia, pp. 153-162, (2013)
  • [14] Geng J., Fan J.-C., Chu J.-L., Wang H.-Y., Research on marine floating raft aquaculture SAR image target recognition based on deep collaborative sparse coding network, Acta Automatica Sinica, 42, 4, pp. 593-604, (2016)
  • [15] Mohamed A.R., Dahl G.E., Hinton G., Acoustic modeling using deep belief networks, IEEE Transactions on Audio, Speech, and Language Processing, 20, 1, pp. 14-22, (2012)
  • [16] Sarikaya R., Hinton G.E., Deoras A., Application of deep belief networks for natural language understanding, IEEE/ACM Transactions on Audio, Speech, and Language Processing, 22, 4, pp. 778-784, (2014)
  • [17] Humphrey E.J., Bello J.P., Lecun Y., Feature learning and deep architectures: new directions for music informatics, Journal of Intelligent Information Systems, 41, 3, pp. 461-481, (2013)
  • [18] Yu J.C., Weng K.J., Liang G.Y., Xie G.H., A vision-based robotic grasping system using deep learning for 3D object recognition and pose estimation, Proceedings of the 2013 IEEE International Conference on Robotics and Biomimetics, pp. 1175-1180, (2013)
  • [19] Noda K., Arie H., Suga Y., Ogata T., Multimodal integration learning of object manipulation behaviors using deep neural networks, Proceedings of the 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1728-1733, (2013)
  • [20] Lenz I., Lee H., Saxena A., Deep learning for detecting robotic grasps, The International Journal of Robotics Research, 34, 4-5, pp. 705-724, (2015)
123