Abstract
Humanoid robots needs to have human-like motions and appearance in order to be well-accepted by humans. Mimicking is a fast and user-friendly way to teach them human-like motions. However, direct assignment of observed human motions to robot’s joints is not possible due to their physical differences. This paper presents a real-time inverse kinematics based human mimicking system to map human upper limbs motions to robot’s joints safely and smoothly. It considers both main definitions of motion similarity, between end-effector motions and between angular configurations. Microsoft Kinect sensor is used for natural perceiving of human motions. Additional constraints are proposed and solved in the projected null space of the Jacobian matrix. They consider not only the workspace and the valid motion ranges of the robot’s joints to avoid self-collisions, but also the similarity between the end-effector motions and the angular configurations to bring highly human-like motions to the robot. Performance of the proposed human mimicking system is quantitatively and qualitatively assessed and compared with the state-of-the-art methods in a human-robot interaction task using Nao humanoid robot. The results confirm applicability and ability of the proposed human mimicking system to properly mimic various human motions.
Similar content being viewed by others
References
Almetwally, I., Mallem, M.: Real-time tele-operation and tele-walking of humanoid Robot Nao using Kinect Depth Camera. In: 10th IEEE international conference on networking, sensing and control (ICNSC) 2013, pp. 463-466
Ningjia, Y., Feng, D., Yudi, W., Chuang, L., Tan, J.T.C., Binbin, X., Jin, Z.: A study of the human-robot synchronous control system based on skeletal tracking technology. In: IEEE International Conference on Robotics and Biomimetics (ROBIO) 2013, pp. 2191-2196
Siscart, M.J.R., Gibert, M.G., Alenyá, G., Industrial, I.D.R.I.I.: Algorithms and graphic interface design to control and teach a humanoid robot through human imitation. Universitat Politécnica de Catalunya (2011)
Luo, R.C., Shih, B.-H., Lin, T.-W.: Real time human motion imitation of anthropomorphic dual arm robot based on Cartesian impedance control. In: IEEE international symposium on robotic and sensors environments (ROSE) 2013, pp. 25-30
Mota, E., Moreira, A.P., do Nascimento, T.P.: Motion and Teaching of a NAO Robot. Provas de Dissertacao do MIEEC, Portugal (2011)
Wang, F., Cheng, T., Yongsheng, O., Yangsheng, X.: A real-time human imitation system. In: 10th World Congress on Intelligent Control and Automation (WCICA), 6-8 July 2012, pp. 3692-3697
Kurt, B.: Imitation of human arm movements by a humanoid robot using monocular vision. Bogaziçi University, Master of Science (2009)
Billard, A., Calinon, S., Dillmann, R., Schaal, S.: Handbook of Robotics Chapter 59: Robot Programming by Demonstration. Handbook of Robotics Springer (2008)
Kemp, C.C., Edsinger, A., Torres-Jara, E.: Challenges for robot manipulation in human environments. IEEE Robot. Autom. Mag. 14(1), 20–29 (2007)
Li, Z., Yang, C., Su, C. -Y., Deng, S., Sun, F., Zhang, W.: Decentralized fuzzy control of multiple cooperating robotic manipulators with impedance interaction. IEEE Trans. Fuzzy Syst. 23(4), 1044–1056 (2015)
He, W., Chen, Y., Yin, Z.: Adaptive neural network control of an uncertain robot with full-state constraints. IEEE Transactions on Cybernetics 46(3), 620–629 (2016)
He, W., David, A.O., Yin, Z., Sun, C.: Neural network control of a robotic manipulator with input deadzone and output constraint. IEEE Trans. Syst. Man Cybern. Syst. Hum. 99, 1–12 (2016)
He, W., Dong, Y., Sun, C.: Adaptive Neural Impedance Control of a Robotic Manipulator With Input Saturation. IEEE Trans. Syst. Man Cybern. Syst. Hum. 46(3), 334–344 (2016). doi:10.1109/TSMC.2015.2429555
Zannatha, J.M.I., Tamayo, A.J.M., Sanchez, A.D.G., Delgado, J.E.L., Cheu, L.E.R., Arevalo, W.A.S.: Development of a system based on 3D vision, interactive virtual environments, ergonometric signals and a humanoid for stroke rehabilitation. Comput. Methods Prog. Biomed 112(2), 239–249 (2013)
Microsoft: Kinect for Windows. (2015)
Aldebaran: NAO Humanoid Robot. https://www.aldebaran.com/en 2015
Krüger, B., Baumann, J., Abdallah, M., Weber, A.: A Study On Perceptual Similarity of Human Motions. In: Workshop on Virtual Reality Interaction and Physical Simulation (VRIPHYS) 2011, pp. 65-72
Tang, J.K., Leung, H., Komura, T., Shum, H.P.: Emulating human perception of motion similarity. Comput. Anim. Virtual Worlds 19(3-4), 211–221 (2008)
Zuher, F., Romero, R.: Recognition of Human Motions for Imitation and Control of a Humanoid Robot. In: Brazilian robotics symposium and latin american robotics symposium (SBR-LARS), 16-19 Oct. 2012, pp. 190-195
Riley, M., Ude, A., Wade, K., Atkeson, C.G.: Enabling real-time full-body imitation: a natural way of transferring human movement to humanoids. In: IEEE international conference on robotics and automation (ICRA’03) 2003, pp. 2368-2374
Ude, A., Man, C., Riley, M., Atkeson, C.G.: Automatic generation of kinematic models for the conversion of human motion capture data into humanoid robot motion. In: Proceedings of the first IEEE-RAS conference on humanoid robotics (Humanoids) 2000, pp. 1-9
Do, M., Azad, P., Asfour, T., Dillmann, R.: Imitation of human motion on a humanoid robot using non-linear optimization. In: 8th IEEE-RAS international conference on humanoids 2008, pp. 545-552
Terlemez, O., Ulbrich, S., Mandery, C., Do, M., Vahrenkamp, N., Asfour, T.: Master Motor Map (MMM)—Framework and toolkit for capturing, representing, and reproducing human motion on humanoid robots. In: 14th IEEE-RAS International Conference on Humanoid Robots (Humanoids), 2014, pp. 894-901
Koenemann, J., Burget, F., Bennewitz, M.: Real-time imitation of human whole-body motions by humanoids. In: IEEE international conference on robotics and automation (ICRA) 2014, pp. 2806-2812
Nakaoka, S., Nakazawa, A., Yokoi, K., Hirukawa, H., Ikeuchi, K.: Generating whole body motions for a biped humanoid robot from captured human dances. In: IEEE international conference on robotics and automation (ICRA’03) 2003, pp. 3905-3910
Yamane, K., Hodgins, J.: Simultaneous tracking and balancing of humanoid robots for imitating human motion capture data. In: IEEE/RSJ international conference on intelligent robots and systems (IROS) 2009, pp. 2510-2517
Sakka, S., Poubel, L.P., Cehajic, D.: Tasks prioritization for whole-body realtime imitation of human motion by humanoid robots. In: Digital Intelligence (DI2014), September 2014, pp. 1-5
Kim, C., Kim, D., Oh, Y.: Adaptation of human motion capture data to humanoid robots for motion imitation using optimization. Integrated computer-aided engineering 13(4), 377–389 (2006)
Tosun, T., Mead, R., Stengel, R.: A general method for kinematic retargeting: adapting poses between humans and robots. In: ASME 2014 international mechanical engineering congress and exposition 2014, pp. V04AT04A027-V004AT004A027
Microsoft: Joint Filtering. https://msdn.microsoft.com/en-us/library/jj131024.aspx 2015
De Leva, P.: Adjustments to Zatsiorsky-Seluyanov’s segment inertia parameters. J. Biomech. 29(9), 1223–1230 (1996)
Aldebaran: H25 - Links - NAO V 3.2. http://doc.aldebaran.com/1-14/family/naoh25/linksh25v32.html#h25-links-v32 2015
Kim, S., Shukla, A., Billard, A.: Catching objects in flight. IEEE Trans. Robot. 30(5), 1049–1065 (2014)
Guan, Y., Yokoi, K.: Reachable space generation of a humanoid robot using the monte carlo method. In: IEEE/RSJ international conference on intelligent robots and systems 2006, pp. 1984-1989
Zacharias, F., Borst, C., Hirzinger, G.: Capturing robot workspace structure: representing robot capabilities. In: IEEE/RSJ international conference on intelligent robots and systems (IROS) 2007, pp. 3229-3236
Kofinas, N., Orfanoudakis, E., Lagoudakis, M.G.: Complete analytical forward and inverse kinematics for the NAO humanoid robot. J. Intell. Robot. Syst. 77(2), 251–264 (2015)
Aldebaran: H25 - Joints - NAO V 3.2. http://doc.aldebaran.com/1-14/family/naoh25/jointsh25v32.html#h25-joints-v32 2015
Jazar, R.N.: Theory of applied robotics: kinematics, dynamics, and control. Springer Science and Business Media (2010)
Sciavicco, L., Siciliano, B., Villani, L., Oriolo, G.: Robotics: modelling, planning and control. In: Springer London, 2009
Aldebaran: NAOqi Motion. http://doc.aldebaran.com/1-14/naoqi/motion/index.html#almotion 2016
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Alibeigi, M., Rabiee, S. & Ahmadabadi, M.N. Inverse Kinematics Based Human Mimicking System using Skeletal Tracking Technology. J Intell Robot Syst 85, 27–45 (2017). https://doi.org/10.1007/s10846-016-0384-6
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10846-016-0384-6