nach oben

Erschienen in:

2019 | OriginalPaper | Buchkapitel

Humanoid Robot Control Based on Deep Learning

verfasst von : Bin Guo, Pengfei Yi, Dongsheng Zhou, Xiaopeng Wei

Erschienen in: E-Learning and Games

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

The direct control of humanoid robot by human motion is an important aspect of current research. Most of these methods are based on additional equipments, such as Kinect, which are usually not equipped on robot. In order to avoid using these external equipments, we explored a robot controlling method only using the low-resolution camera on robot. Firstly, a stacked hourglass network is employed to obtain the accurate 2D heatmap containing positions of human joints from RGB image captured by camera on robot. Then, 3D human poses including coordinates of human body joints are estimated from 2D heatmaps by a method aiming to reconstruct 3D human poses from 2D poses. Finally, the rotation angles of robot are computed according to these 3D coordinates and are transmitted to the robot to reconstruct the original human pose. Using the NAO robot as an example, the experimental results show that the humanoid robot can imitate motions of different human actors in different scenes well while applying our method.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Vorheriges Kapitel A Novel Feature-Based Pose Estimation Method for 3D Faces

Nächstes Kapitel Improved Modular Convolution Neural Network for Human Pose Estimation

Abdallah, I.B., Bouteraa, Y., Rekik, C.: Kinect-based sliding mode control for Lynxmotion robotic arm. Adv. Hum.-Comput. Interact. 2016, 1–10 (2016)CrossRef

Guo, M., Das, S., Bumpus, J., Bekele E., Sarkar, N.: Interfacing of kinect motion sensor and NAO humanoid robot for imitation learning. Young Scientist (2013)

Nie, B.X., Xiong, C., Zhu, S.: Joint action recognition and pose estimation from video. In: Computer Vision and Pattern Recognition, pp. 1293–1301 (2015)

Simonyan, K., Zisserman, A.: Two-stream convolutional networks for action recognition in videos. In: Neural Information Processing Systems, pp. 568–576 (2014)

Chen, Y., Shen, C.H., Liu, L.Q., Yang, J., Wei, X.S.: Adversarial PoseNet: a structure-aware convolutional network for human pose estimation. In: IEEE International Conference on Computer Vision, pp. 1221–1230 (2017)

Alwasel, A., Elrayes, K., Abdel-Rahman, E., Haas, C.: A human body posture sensor for monitoring and diagnosing MSD risk factors. In: Proceedings of the 30th ISARC, Montreal, Canada, pp. 531–539 (2013)

Sharma, R.P., Verma, G.K.: Human computer interaction using hand gesture. Procedia Comput. Sci. 54, 721–727 (2015)CrossRef

Sapp, B., Taskar, B.: MODEC: multimodal decomposable models for human pose estimation. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 3674–3681 (2013)

Pishchulin, L., Andriluka, M., Gehler, P.V., Schiele, B.: Strong appearance and expressive spatial models for human pose estimation. In: International Conference on Computer Vision, pp. 3487–3494 (2013)

10.

Yang, Y., Ramanan, D.: Articulated human detection with flexible mixtures of parts. IEEE Trans. Pattern Anal. Mach. Intell. 35, 2878–2890 (2013)CrossRef

11.

Toshev, A., Szegedy, C.: DeepPose: human pose estimation via deep neural networks. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1653–1660 (2014)

12.

Wei, S., Ramakrishna, V., Kanade, T., Sheikh, Y.: Convolutional pose machines. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 4724–4732 (2016)

13.

Pfister, T., Charles, J., Zisserman, A.: Flowing ConvNets for human pose estimation in videos. In: International Conference on Computer Vision, pp. 1913–1921 (2015)

14.

Newell, A., Yang, K., Deng, J.: Stacked hourglass networks for human pose estimation. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9912, pp. 483–499. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46484-8_29CrossRef

15.

Andriluka, M., Pishchulin, L., Gehler, P., Schiele, B.: 2D human pose estimation: new benchmark and state of the art analysis. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 3686–3693 (2014)

16.

Li, S., Chan, A.B.: 3D human pose estimation from monocular images with deep convolutional neural network. In: Cremers, D., Reid, I., Saito, H., Yang, M.-H. (eds.) ACCV 2014. LNCS, vol. 9004, pp. 332–347. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-16808-1_23CrossRef

17.

Zhou, X., Zhu, M., Leonardos, S., Daniilidis, K.: Sparse representation for 3D shape estimation: a convex relaxation approach. IEEE Trans. Pattern Anal. Mach. Intell. 39, 1648–1661 (2017)CrossRef

18.

Tekin, B., Rozantsev, A., Lepetit, V.: Direct prediction of 3D body poses from motion compensated sequences. In: Computer Vision and Pattern Recognition, pp. 991–1000 (2016)

19.

Chen, X., Yuille, A.: Articulated pose estimation by a graphical model with image dependent pairwise relations. In: Neural Information Processing Systems, pp. 1736–1744 (2014)

20.

Tompson, J., et al.: Joint training of a convolutional network and a graphical model for human pose estimation. In: Advances in Neural Information Processing Systems, pp. 1799–1807 (2014)

21.

Yasin, H., Yasin, H., Iqbal, U., Kruger, B., Weber, A., Gall, J.: A dual-source approach for 3D pose estimation from a single image. In: Computer Vision and Pattern Recognition, pp. 4948–4956 (2016)

22.

Zhu, Y., Huang, D., De La Torre, F., Lucey, S.: Complex non-rigid motion 3D reconstruction by union of subspaces. In: Computer Vision and Pattern Recognition, pp. 1542–1549 (2014)

23.

Zhou, X., Zhu, M., Leonardos, S., Derpanis, K.G., Daniilidis, K.: Sparseness meets deepness: 3D human pose estimation from monocular video. In: Computer Vision and Pattern Recognition, pp. 4966–4975 (2016)

24.

Akhter, I., Black, M.J.: Pose-conditioned joint angle limits for 3D human pose reconstruction. In: Computer Vision and Pattern Recognition, pp. 1446–1455 (2015)

25.

Ramakrishna, V., Kanade, T., Sheikh, Y.: Reconstructing 3D human pose from 2D image landmarks. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, vol. 7575, pp. 573–586. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33765-9_41CrossRef

26.

Ionescu, C., Papava, D., Olaru, V., Sminchisescu, C.: Human3.6M: large scale datasets and predictive methods for 3D human sensing in natural environments. IEEE Trans. Pattern Anal. Mach. Intell. 36, 1325–1339 (2014)CrossRef

Titel: Humanoid Robot Control Based on Deep Learning
verfasst von: Bin Guo
Pengfei Yi
Dongsheng Zhou
Xiaopeng Wei
Verlag: Springer International Publishing
Buch: E-Learning and Games
Print ISBN: 978-3-030-23711-0

Electronic ISBN: 978-3-030-23712-7

Copyright-Jahr: 2019
DOI: https://doi.org/10.1007/978-3-030-23712-7_52

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Premium Partner