Skip to main content
×
×
Home

Human to humanoid motion conversion for dual-arm manipulation tasks

  • Marija Tomić (a1) (a2), Christine Chevallereau (a2), Kosta Jovanović (a1), Veljko Potkonjak (a1) and Aleksandar Rodić (a3)...
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.

Copyright
Corresponding author
*Corresponding author. E-mail: marija.tomic@pupin.rs
References
Hide All
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.
2. Vlasic, D. et al., “Practical motion capture in everyday surroundings,” ACM Trans. Graphics 26 (3), 35 (2007).
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.
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), 407426 (2015).
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).
6. Zhou, H. and Hu, H., “Human motion tracking for rehabilitation-A survey,” Biomed. Signal Process. Control 3 (1), 118 (2008).
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), 875883 (2016).
8. Billard, A. et al., “Discovering optimal imitation strategies,” Robot. Auton. Syst. 47 (2), 6977 (2004).
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.
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.
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), 737745 (2010).
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), 205218 (2016). https://doi.org/10.1007/s10846-016-0361-0.
13. Ude, A., Atkeson, C. G. and Riley, M., “Programming full-body movements for humanoid robots by observation,” Robot. Auton. Syst. 47 (2), 93108 (2004).
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.
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, 274284 (2014).
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.
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. 227238.
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.
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).
20. FUI national Romeo project: http://projetromeo.com. (last visited 2nd November 2016).
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).
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.
23. Siciliano, B., Sciavicco, L., Villani, L. and Oriolo, G., Robotics: Modelling, Planning and Control (Springer Science and Business Media-Verlag London, 2010).
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).
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.
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. 141183.
27. Wampler, C. W., “Manipulator inverse kinematic solutions based on vector formulations and damped least-squares methods,” IEEE Trans. Syst. Man Cybern. 16 (1), 93101 (1986).
28. Baerlocher, P. and Boulic, R., “An inverse kinematics architecture enforcing an arbitrary number of strict priority levels,” The Visual Comput. 20 (6), 402417 (2004).
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.
30. Orfanidis, S. J., Introduction to Signal Processing (Prentice-Hall, Upper Saddle River, New Jersey, 1996).
31. Mühlig, M., Gienger, M. and Steil, J. J., “Interactive imitation learning of object movement skills,” Auton. Robots 32 (2), 97114 (2012).
32. Dariush, B. et al., “Online transfer of human motion to humanoids,” Int. J. Humanoid Robot. 6 (2), 265289 (2009).
33. Ruchanurucks, M., “Humanoid robot upper body motion generation using B-spline-based functions,” Robotica 33 (04), 705720 (2015).
34. Qu, J., Zhang, F., Fu, Y. and Guo, S., “Multi-cameras visual servoing for dual-arm coordinated manipulation,” Robotica 35 (11), 22182237 (2017). doi: 10.1017/S0263574716000849.
35. Khalil, W. and Creusot, D., “SYMORO+: A system for the symbolic modelling of robots,” Robotica 15, 153161 (1997).
Recommend this journal

Email your librarian or administrator to recommend adding this journal to your organisation's collection.

Robotica
  • ISSN: 0263-5747
  • EISSN: 1469-8668
  • URL: /core/journals/robotica
Please enter your name
Please enter a valid email address
Who would you like to send this to? *
×

Keywords

Metrics

Full text views

Total number of HTML views: 0
Total number of PDF views: 86 *
Loading metrics...

Abstract views

Total abstract views: 260 *
Loading metrics...

* Views captured on Cambridge Core between 25th April 2018 - 15th August 2018. This data will be updated every 24 hours.