Abstract
Exploring the full potential of humanoid robots requires their ability to learn, generalize and reproduce complex tasks that will be faced in dynamic environments. In recent years, significant attention has been devoted to recovering kinematic information from the human motion using a motion capture system. This paper demonstrates and evaluates the use of a Kinect-based capture system that estimates the 3D human poses and converts them into gestures imitation in a robot. The main objectives are twofold: (1) to improve the initially estimated poses through a correction method based on constraint optimization, and (2) to present a method for computing the joint angles for the upper limbs corresponding to motion data from a human demonstrator. The feasibility of the approach is demonstrated by experimental results showing the upper-limb imitation of human actions by a robot model.
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References
Billard, A., Callinon, S., Dillmann, R., Schaal, S.: Robot Programming by Demonstration. In: Siciliano, B., Khatib, O. (eds.) Handbook of Robotics, Springer, New York (2008)
Argall, B.D., Chernova, S., Veloso, M., Browning, B.: A Survey of Robot Learning from Demonstration. Robotics and Autonomous Systems 57(5), 469–483 (2009)
Dasgupta, A., Nakamura, Y.: Making Feasible Walking Motion of Humanoid Robots from Human Motion Capture Data. In: IEEE International Conference on Robotics and Automation, pp. 1044–1049 (1999)
Elgammal, A., Lee, C.-S.: Tracking People on a Torus. IEEE Transactions on Pattern Analysis and Machine Intelligence 31(3), 520–538 (2009)
Inamura, T., Toshima, I., Tanie, H., Nakamura, Y.: Embodied Symbol Emergence Based on Mimesis Theory. International Journal of Robotics Research 23(4-5), 363–377 (2004)
Kulic, D., Takano, J.W., Nakamura, Y.: Incremental Learning, Clustering and Hierarchy Formation of Whole Body Motion Patterns using Adaptive Hidden Markov Chains. International Journal of Robotics Research 27(7), 761–784 (2008)
Shon, A.P., Grochow, K., Hertzmann, A.: Rao. R.P.: Learning Shared Latent Structure for Image Synthesis and Robotic Imitation. In: Weiss, Y., Schlkopf, B., Platt, J.C. (eds.) Advances in Neural Information Processing Systems. MIT Press, Cambridge (2005)
Shotton, J., Fitzgibbon, A.W., Cook, M., Sharp, T., Finocchio, M., Moore, R., Kipman, A., Blake, A.: “Real-time Human Pose Recognition in Parts from Single Depth Images. In: IEEE Computer Vision and Pattern Recognition. Colorado Springs, USA (2011)
Khoshelham, K., Elberink, S.O.: Accuracy and Resolution of Kinect Depth Data for Indoor Mapping Applications. Sensors 12(2), 1437–1454 (2012)
Smisek, J., Jancosek, M., Pajdla, T.: 3D with Kinect. In: International Conference on Computer Vision Workshops, Barcelona, Spain, pp. 1154–1160 (2011)
Obdržálek, S., Kurillo, G., Ofli, F., Bajcsy, R., Seto, E., Jimison, H., Pavel, M.: Accuracy and Robustness of Kinect Pose Estimation in the Context of Coaching of Elderly Population. In: International Conference of the IEEE Engineering in Medicine and Biology Society, California, USA, pp. 1188–1193 (2012)
Platz, T., Denzler, P., Kaden, B., Mauritz, K.-H.: Motor Learning After Recovery from Hemiparesis. Neuropsychologia 32, 1209–1223 (1994)
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Rosado, J., Silva, F., Santos, V. (2014). A Kinect-Based Motion Capture System for Robotic Gesture Imitation. In: Armada, M., Sanfeliu, A., Ferre, M. (eds) ROBOT2013: First Iberian Robotics Conference. Advances in Intelligent Systems and Computing, vol 252. Springer, Cham. https://doi.org/10.1007/978-3-319-03413-3_43
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DOI: https://doi.org/10.1007/978-3-319-03413-3_43
Publisher Name: Springer, Cham
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