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Human to humanoid motion conversion for dual-arm manipulation tasks

Published online by Cambridge University Press:  25 April 2018

Marija Tomić ,
Christine Chevallereau ,
Kosta Jovanović ,
Veljko Potkonjak  and
Aleksandar Rodić
Marija Tomić*
Affiliation:
School of Electrical Engineering, University of Belgrade, 11000, Belgrade, Serbia E-mails: kostaj@etf.rs, potkonjak@yahoo.com LS2N, CNRS, Ecole Centrale de Nantes, 44321, Nantes, France E-mail: Christine.Chevallereau@ls2n.fr
Christine Chevallereau
Affiliation:
LS2N, CNRS, Ecole Centrale de Nantes, 44321, Nantes, France E-mail: Christine.Chevallereau@ls2n.fr
Kosta Jovanović
Affiliation:
School of Electrical Engineering, University of Belgrade, 11000, Belgrade, Serbia E-mails: kostaj@etf.rs, potkonjak@yahoo.com
Veljko Potkonjak
Affiliation:
School of Electrical Engineering, University of Belgrade, 11000, Belgrade, Serbia E-mails: kostaj@etf.rs, potkonjak@yahoo.com
Aleksandar Rodić
Affiliation:
Robotics Laboratory, IMP, 11000, Belgrade, Serbia E-mail: aleksandar.rodic@pupin.rs
*
*Corresponding author. E-mail: marija.tomic@pupin.rs
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Summary

A conversion process for the imitation of human dual-arm motion by a humanoid robot is presented. The conversion process consists of an imitation algorithm and an algorithm for generating human-like motion of the humanoid. The desired motions in Cartesian and joint spaces, obtained from the imitation algorithm, are used to generate the human-like motion of the humanoid. The proposed conversion process improves existing techniques and is developed with the aim to enable imitating of human motion with a humanoid robot, to perform a task with and/or without contact between hands and equipment. A comparative analysis shows that our algorithm, which takes into account the situation of marker frames and the position of joint frames, ensures more precise imitation than previously proposed methods. The results of our conversion algorithm are tested on the robot ROMEO through a complex “open/close drawer” task.

Keywords

Motion capture systemDual arm manipulationImitation algorithmsHumanoid robotMotion planning
Type
Articles
Information
Robotica , Volume 36 , Issue 8 , August 2018 , pp. 1167 - 1187
Copyright
Copyright © Cambridge University Press 2018 

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References

1. Do, M., Azad, P., Asfour, T. and Dillmann, R., “Imitation of Human Motion on a Humanoid Robot Using Non-Linear Optimization,” Proceeding of the Humanoids 2008-8th IEEE-RAS International Conference on Humanoid Robots (2008) pp. 545–552.Google Scholar
2. Vlasic, D. et al., “Practical motion capture in everyday surroundings,” ACM Trans. Graphics 26 (3), 35 (2007).Google Scholar
3. Miller, N., Jenkins, O. C., Kallmann, M. and Mataric, M. J., “Motion Capture from Inertial Sensing for Untethered Humanoid Teleoperation,” Proceeding of the 2004 4th IEEE/RAS International Conference on Humanoid Robots, vol. 2, (2004) pp. 547–565.Google Scholar
4. Aloui, S., Villien, C. and Lesecq, S., “A new approach for motion capture using magnetic field: Models, algorithms and first results,” Int. J. Adapt. Control Signal Process. 29 (4), 407–426 (2015).Google Scholar
5. Ceseracciu, E., Sawacha, Z. and Cobelli, C., “Comparison of markerless and marker-based motion capture technologies through simultaneous data collection during gait: Proof of concept,” PLoS One 9 (3), e87640 (2014).Google Scholar
6. Zhou, H. and Hu, H., “Human motion tracking for rehabilitation-A survey,” Biomed. Signal Process. Control 3 (1), 1–18 (2008).Google Scholar
7. Gómez, M. J., Castejón, C., Garcia-Prada, J. C., Carbone, G. and Ceccarelli, M., “Analysis and comparison of motion capture systems for human walking,” Exp. Tech. 40 (2), 875–883 (2016).Google Scholar
8. Billard, A. et al., “Discovering optimal imitation strategies,” Robot. Auton. Syst. 47 (2), 69–77 (2004).CrossRefGoogle Scholar
9. Ott, C., Lee, D. and Nakamura, Y., “Motion Capture Based Human Motion Recognition and Imitation by Direct Marker Control,” Proceedings of the IEEE-RAS International Conference on Humanoid Robots (2008) pp. 399–405.Google Scholar
10. Suleiman, W. et al., “On Human Motion Imitation by Humanoid Robot,” Proceedings of the IEEE International Conference on Robotics and Automation ICRA 2008 (2008) pp. 2697–2704.Google Scholar
11. Huang, Q., Yu, Z., Zhang, W., Xu, W. and Chen, X., “Design and similarity evaluation on humanoid motion based on human motion capture,” Robotica 28 (5), 737–745 (2010).Google Scholar
12. Jamisola, R. S., Kormushev, P. S., Roberts, R. G. et al., “Task-space modular dynamics for dual-arms expressed through a relative Jacobian,” J. Intell. Robot. Syst. 83 (2), 205–218 (2016). https://doi.org/10.1007/s10846-016-0361-0.Google Scholar
13. Ude, A., Atkeson, C. G. and Riley, M., “Programming full-body movements for humanoid robots by observation,” Robot. Auton. Syst. 47 (2), 93–108 (2004).Google Scholar
14. Ude, A., Man, C., Riley, M. and Atkeson, C. G., “Automatic Generation of Kinematic Models for the Conversion of Human Motion Capture Data into Humanoid Robot Motion,” Proceeding of the 1st IEEE-RAS International Conference on Humanoid Robots (2000) pp. 2223–2228.Google Scholar
15. Ayusawa, K., Ikegami, Y. and Nakamura, Y., “Simultaneous global inverse kinematics and geometric parameter identification of human skeletal model from motion capture data,” Mech. Mach. Theory 74, 274–284 (2014).Google Scholar
16. Ayusawa, K., Morisawa, M. and Yoshida, E., “Motion Retargeting for Humanoid Robots Based on Identification to Preserve and Reproduce Human Motion Features,” Proceeding of the Intelligent Robots and Systems IROS2015 (2015) pp. 2774–2779.Google Scholar
17. Tomić, M., Vassallo, C., Chevallereau, C. et al., “Arm Motions of a Humanoid Inspired by Human Motion,” In: New Trends in Medical and Service Robots (Bleuler, H., Bouri, M., Mondada, F., Pisla, D., Rodic, A., Helmer, P., eds.) (Springer International Publishing AG Switzerland, 2016) pp. 227–238.Google Scholar
18. Khalil, W. and Kleinfinger, J., “A New Geometric Notation for Open and Closed-Loop Robots,” Proceedings of the IEEE International Conference on Robotics and Automation, vol. 3, (1986) pp. 1174–1179.Google Scholar
19. ART GmbH, “System user manual ARTtrack,” TRACKPACK and DTrack, (2015), version 2.11.: http://www.schneider-digital.com/support/download/Tools-Ressourcen/ARTTracking/Dokumentation/ARTtrackDTrackTrackPACKUserManual2.11.pdf/. (last visited 2nd November 2016).Google Scholar
20. FUI national Romeo project: http://projetromeo.com. (last visited 2nd November 2016).Google Scholar
21. Köhler, H., Pruzinec, M., Feldmann, T. and Worner, A., “Automatic Human Model Parametrization from 3D Marker Data for Motion Recognition,” Proceedings of the International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision (2008).Google Scholar
22. Kirk, A. G., O'Brien, J. F. and Forsyth, D. A., “Skeletal Parameter Estimation from Optical Motion Capture Data,” Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, (2005) pp. 782–788.Google Scholar
23. Siciliano, B., Sciavicco, L., Villani, L. and Oriolo, G., Robotics: Modelling, Planning and Control (Springer Science and Business Media-Verlag London, 2010).Google Scholar
24. Jovanović, K., Potkonjak, V. and Holland, O., “Dynamic modeling of an anthropomimetic robot in contact tasks,” Adv. Robot. 28 (11), pp. 793–806 (2014).Google Scholar
25. Bagheri, M., “Kinematic Analysis and Design Considerations for Optimal Base Frame Arrangement of Humanoid Shoulders,” Proceedings of the 2015 IEEE International Conference on Robotics and Automation ICRA2015 (2015) pp. 2710–2715.Google Scholar
26. Wenger, P., “Performance Analysis of Robots,” In: Modeling, Performance Analysis and Control of Robot Manipulators (Dombre, E. and Khalil, W. eds.) (iSTE, Great Britain, 2010) pp. 141–183.Google Scholar
27. Wampler, C. W., “Manipulator inverse kinematic solutions based on vector formulations and damped least-squares methods,” IEEE Trans. Syst. Man Cybern. 16 (1), 93–101 (1986).Google Scholar
28. Baerlocher, P. and Boulic, R., “An inverse kinematics architecture enforcing an arbitrary number of strict priority levels,” The Visual Comput. 20 (6), 402–417 (2004).Google Scholar
29. Safonova, A., Pollard, N. and Hodgins, J. K., “Optimizing human motion for the control of a humanoid robot,” Proceedings of International Conference on Robotics and Automation, (2003) pp. 1390–1397.Google Scholar
30. Orfanidis, S. J., Introduction to Signal Processing (Prentice-Hall, Upper Saddle River, New Jersey, 1996).Google Scholar
31. Mühlig, M., Gienger, M. and Steil, J. J., “Interactive imitation learning of object movement skills,” Auton. Robots 32 (2), 97–114 (2012).Google Scholar
32. Dariush, B. et al., “Online transfer of human motion to humanoids,” Int. J. Humanoid Robot. 6 (2), 265–289 (2009).Google Scholar
33. Ruchanurucks, M., “Humanoid robot upper body motion generation using B-spline-based functions,” Robotica 33 (04), 705–720 (2015).Google Scholar
34. Qu, J., Zhang, F., Fu, Y. and Guo, S., “Multi-cameras visual servoing for dual-arm coordinated manipulation,” Robotica 35 (11), 2218–2237 (2017). doi: 10.1017/S0263574716000849.Google Scholar
35. Khalil, W. and Creusot, D., “SYMORO+: A system for the symbolic modelling of robots,” Robotica 15, 153–161 (1997).CrossRefGoogle Scholar