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
  • [1] Paolini R., Rodriguez A., Srinivasa S.S., Mason M.T., A data-driven statistical framework for post-grasp manipulation, The International Journal of Robotics Research, 33, 4, pp. 600-615, (2014)
  • [2] Jia B.-X., Liu S., Zhang K.-X., Chen J., Survey on robot visual servo control: vision system and control strategies, Acta Automatica Sinica, 41, 5, pp. 861-873, (2015)
  • [3] Droniou A., Ivaldi S., Sigaud O., Deep unsupervised network for multimodal perception, representation and classification, Robotics and Autonomous Systems, 71, 9, pp. 83-98, (2015)
  • [4] Gao Y.-Y., Zhu W.-B., Deep neural networks with visible intermediate layers, Acta Automatica Sinica, 41, 9, pp. 1627-1637, (2015)
  • [5] Qiao J.-F., Pan G.-Y., Han H.-G., Design and application of continuous deep belief network, Acta Automatica Sinica, 41, 12, pp. 2138-2146, (2015)
  • [6] Hinton G.E., Salakhutdinov R.R., Reducing the dimensionality of data with neural networks, Science, 313, 5786, pp. 504-507, (2006)
  • [7] Bengio Y., Learning deep architectures for AI, Foundations and Trendsr in Machine Learning, 2, 1, pp. 1-127, (2009)
  • [8] Langkvist M., Karlsson L., Loutfi A., A review of unsupervised feature learning and deep learning for time-series modeling, Pattern Recognition Letters, 42, pp. 11-24, (2014)
  • [9] Erhan D., Bengio Y., Courville A., Manzagol P.A., Vincent P., Bengio S., Why does unsupervised pre-training help deep learning?, Journal of Machine Learning Research, 11, pp. 625-660, (2010)
  • [10] Salakhutdinov R., Hinton G., Deep Boltzmann machines, Proceedings of the 12th International Conference on Artificial Intelligence and Statistics (AISTATS) 2009, pp. 448-455, (2009)
123