Hostname: page-component-7dd5485656-7jgsp Total loading time: 0 Render date: 2025-10-31T20:08:45.153Z Has data issue: false hasContentIssue false
  • English
  • Français

Kinesthetic Guidance Utilizing DMP Synchronization and Assistive Virtual Fixtures for Progressive Automation

Published online by Cambridge University Press:  14 October 2019

Dimitrios Papageorgiou ,
Fotios Dimeas Open the ORCID record for Fotios Dimeas [Opens in a new window] ,
Theodora Kastritsi  and
Zoe Doulgeri
Dimitrios Papageorgiou
Affiliation:
Automation & Robotics Lab, Department of Electrical & Computer Engineering, Aristotle University of Thessaloniki, Greece. E-mails: dimpapag@eng.auth.gr, tkastrit@ece.auth.gr, doulgeri@eng.auth.gr
Fotios Dimeas*
Affiliation:
Automation & Robotics Lab, Department of Electrical & Computer Engineering, Aristotle University of Thessaloniki, Greece. E-mails: dimpapag@eng.auth.gr, tkastrit@ece.auth.gr, doulgeri@eng.auth.gr
Theodora Kastritsi
Affiliation:
Automation & Robotics Lab, Department of Electrical & Computer Engineering, Aristotle University of Thessaloniki, Greece. E-mails: dimpapag@eng.auth.gr, tkastrit@ece.auth.gr, doulgeri@eng.auth.gr
Zoe Doulgeri
Affiliation:
Automation & Robotics Lab, Department of Electrical & Computer Engineering, Aristotle University of Thessaloniki, Greece. E-mails: dimpapag@eng.auth.gr, tkastrit@ece.auth.gr, doulgeri@eng.auth.gr
*
*Corresponding author. E-mail: dimeasf@ee.auth.gr
Get access Rights & Permissions [Opens in a new window]

Summary

The progressive automation framework allows the seamless transition of a robot from kinesthetic guidance to autonomous operation mode during programming by demonstration of discrete motion tasks. This is achieved by the synergetic action of dynamic movement primitives (DMPs), virtual fixtures, and variable impedance control. The proposed DMPs encode the demonstrated trajectory and synchronize with the current demonstration from the user so that the reference generated motion follows the human’s demonstration. The proposed virtual fixtures assist the user in repeating the learned kinematic behavior but allow penetration so that the user can make modifications to the learned trajectory if needed. The tracking error in combination with the interaction forces and torques is used by a variable stiffness strategy to adjust the progressive automation level and transition the leading role between the human and the robot. An energy tank approach is utilized to apply the designed controller and to prove the passivity of the overall control method. An experimental evaluation of the proposed framework is presented for a pick and place task and results show that the transition to autonomous mode is achieved in few demonstrations.

Keywords

Physical human–robot interactionProgramming by demonstrationDynamic movement primitivesVirtual fixtures

Information

Type
Articles
Information
Robotica , Volume 38 , Special Issue 10: Human–Robot Interaction (HRI) , October 2020 , pp. 1824 - 1841
Copyright
Copyright © Cambridge University Press 2019

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

Pan, Z., Polden, J., Larkin, N., Van Duin, S. and Norrish, J., “Recent progress on programming methods for industrial robots,” Robot Cim.-Int. Manuf . 28(2), 87–94 (2012).CrossRefGoogle Scholar
Dimeas, F., Fotiadis, F., Papageorgiou, D., Sidiropoulos, A. and Doulgeri, Z., “Towards Progressive Automation of Repetitive Tasks Through Physical Human-Robot Interaction,” In: Human Friendly Robotics. Springer Proceedings in Advanced Robotics (Ficuciello, F., Ruggiero, F., and Finzi, A., eds.) (Springer, Cham, 2019) pp. 151–163.CrossRefGoogle Scholar
Thomaz, A. L. and Breazeal, C., “Teachable robots: Understanding human teaching behavior to build more effective robot learners,” Artif. Intell. 172(6–7), 716–737 (2008).CrossRefGoogle Scholar
Dimeas, F. and Doulgeri, Z., “Progressive Automation of Repetitive Tasks Involving both Translation and Rotation,” In: Advances in Service and Industrial Robotics. RAAD 2018. Mechanisms and Machine Science (Aspragathos, N., Koustoumpardis, P. and Moulianitis, V., eds.), vol. 67 (Springer, Cham, 2019) pp. 53–62.Google Scholar
Kastritsi, T., Dimeas, F. and Doulgeri, Z., “Progressive automation with DMP synchronization and variable stiffness control,” IEEE Robot. Autom. Lett. 3(4), 3789–3796 (2018).CrossRefGoogle Scholar
Kastritsi, T., Sidiropoulos, A., and Doulgeri, Z., “A pHRI Framework for Modifying a Robot’s Kinematic Behaviour via Varying Stiffness and Dynamical System Synchronization,” 26th Mediterranean Conference on Control and Automation (MED), Zadar, Croatia (2018) pp. 33–38.Google Scholar
Billard, A. G., Calinon, S. and Dillmann, R., “Learning from Humans,” In: Springer Handbook of Robotics (Springer International Publishing, Cham, 2016) pp. 1995–2014.CrossRefGoogle Scholar
Abbink, D. A., Mulder, M. and Boer, E. R., “Haptic shared control: Smoothly shifting control authority?” Cognit. Technol. Work 14(1), 19–28 (2012).CrossRefGoogle Scholar
Li, Y., Tee, K. P., Chan, W. L., Yan, R., Chua, Y. and Limbu, D. K., “Continuous role adaptation for human-robot shared control,” IEEE Trans. Robot . 31(3), 672–681 (2015).CrossRefGoogle Scholar
Bussy, A., Gergondet, P., Kheddar, A., Keith, F. and Crosnier, A., “Proactive Behavior of a Humanoid Robot in a Haptic Transportation Task with a Human Partner,” IEEE International Workshop on Robot and Human Interactive Communication, Paris (2012) pp. 962–967.Google Scholar
Tykal, M., Montebelli, A. and Kyrki, V., “Incrementally Assisted Kinesthetic Teaching for Programming by Demonstration,” ACM/IEEE International Conference on Human-Robot Interaction, Christchurch (2016) pp. 205–212.Google Scholar
Saveriano, M., An, S. I. and Lee, D., “Incremental Kinesthetic Teaching of End-Effector and Null-Space Motion Primitives,” IEEE International Conference on Robotics and Automation, Seattle, WA (2015) pp. 3570–3575.Google Scholar
Jarrasse, N., Sanguineti, V. and Burdet, E., “Slaves no longer: Review on role assignment for human-robot joint motor action,” Adapt. Behav. 22(1), 70–82 (2013).CrossRefGoogle Scholar
Kronander, K. and Billard, A., “Stability considerations for variable impedance control,” IEEE Trans. Robot . 32(5), 1298–1305 (2016).CrossRefGoogle Scholar
Ferraguti, F., Secchi, C. and Fantuzzi, C., “A Tank-Based Approach to Impedance Control with Variable Stiffness,” 2013 IEEE International Conference on Robotics and Automation, Karlsruhe, Germany (2013) pp. 4948–4953.Google Scholar
Kronander, K. and Billard, A., “Passive interaction control with dynamical systems,” IEEE Robot. Autom. Lett. 1(1), 106–113 (2016).CrossRefGoogle Scholar
Khansari-Zadeh, S. M. and Billard, A., “Learning stable nonlinear dynamical systems with Gaussian mixture models,” IEEE Trans. Robot . 27(5), 943–957 (2011).CrossRefGoogle Scholar
Pastor, P., Kalakrishnan, M., Meier, F., Stulp, F., Buchli, J., Theodorou, E. and Schaal, S., “From dynamic movement primitives to associative skill memories,” Robot. Auton. Syst. 61(4), 351–361 (2013).CrossRefGoogle Scholar
Ijspeert, A. J., Nakanishi, J. and Schaal, S., “Movement Imitation with Nonlinear Dynamical Systems in Humanoid Robots,” In: Proceedings 2002 IEEE International Conference on Robotics and Automation, Washington, DC, USA (2002) pp. 1398–1403.Google Scholar
Ben Amor, H., Neumann, G., Kamthe, S., Kroemer, O. and Peters, J., “Interaction primitives for human-robot cooperation tasks,” IEEE International Conference on Robotics and Automation, Hong Kong, China (2014) pp. 2831–2837.Google Scholar
Pervez, A., Ali, A., Ryu, J. H. and Lee, D., “Novel Learning from Demonstration Approach for Repetitive Teleoperation Tasks,” 2017 IEEE World Haptics Conference, WHC 2017, Munich, Germany (2017) pp. 60–65.Google Scholar
Nakanishi, J., Morimoto, J., Endo, G., Cheng, G., Schaal, S. and Kawato, M., “Learning from demonstration and adaptation of biped locomotion,” Robot. Auton. Syst. 47(2–3), 79–91 (2004).CrossRefGoogle Scholar
Kim, S., Gribovskaya, E. and Billard, A., “Learning Motion Dynamics to Catch a Moving Object,” In: 2010 10th IEEE-RAS International Conference on Humanoid Robots, Nashville, TN (2010) pp. 106–111.Google Scholar
Abbott, J., Marayong, P. and Okamura, A., “Haptic Virtual Fixtures for Robot-Assisted Manipulation,” In: Springer Tracts in Advanced Robotics (Thrun, S., Brooks, R. and Durrant-Whyte, H., eds.) (2007) pp. 49–64.Google Scholar
Rosenberg, L. B., “The use of virtual fixtures as perceptual overlays to enhance operator performance in remote environments.,” Technical Report, DTIC Document (1992).Google Scholar
Colgate, J. E., Peshkin, M. and Klostermeyer, S. H., “Intelligent Assist Devices in Industrial Applications: A Review,” In: Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No.03CH37453), Las Vegas, NV (2003) pp. 2516–2521.Google Scholar
Lin, H. C., Mills, K., Kazanzides, P., Hager, G. D., Marayong, P., Okamura, A. M. and Karam, R., “Portability and applicability of virtual fixtures across medical and manufacturing tasks,” In: Proceedings 2006 IEEE International Conference on Robotics and Automation, ICRA 2006, Orlando, FL (2006) pp. 225–230.Google Scholar
Bettini, A., Marayong, P., Lang, S., Okamura, A. M. and Hager, G. D., “Vision-assisted control for manipulation using virtual fixtures,” IEEE Trans. Robot . 20(6), 953–966 (2004).CrossRefGoogle Scholar
Bowyer, S. A. and y Baena, F. R., “Dissipative control for physical human–robot interaction,” IEEE Trans. Robot. 31(6), 1281–1293 (2015).CrossRefGoogle Scholar
Lee, D. and Ott, C., “Incremental kinesthetic teaching of motion primitives using the motion refinement tube,” Auton. Robots 31(2–3), 115–131 (2011).CrossRefGoogle Scholar
Restrepo, S. S., Raiola, G., Chevalier, P., Lamy, X. and Sidobre, D., “Iterative Virtual Guides Programming for Human-Robot Comanipulation,” IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM, Munich, Germany (2017) pp. 219–226.Google Scholar
Ijspeert, A. J., Nakanishi, J., Hoffmann, H., Pastor, P. and Schaal, S., “Dynamical Movement Primitives: Learning attractor models for motor behaviors,” Neural Computat . 25(2), 328–373 (2013).CrossRefGoogle Scholar
Ude, A., Nemec, B., Petrič, T. and Morimoto, J., “Orientation in Cartesian Space Dynamic Movement Primitives,” IEEE International Conference on Robotics and Automation, Hong Kong, China (2014) pp. 2997–3004.Google Scholar
Siciliano, B., Sciavicco, L., Villani, L. and Oriolo, G., Robotics: Modelling, Planning and Control (Springer, Heidelberg, 2009).CrossRefGoogle Scholar
Ott, C., “Cartesian Impedance Control of Redundant and Flexible-Joint Robots,” In: Springer Tracts in Advanced Robotics (Siciliano, B., Khatib, O., and Groen, F., eds.), vol. 49 (2008).Google Scholar
Khalil, H., Nonlinear Systems (Prentice Hall, Upper Saddle River, 2002).Google Scholar
Schaal, S. and Atkeson, C., “Constructive incremental learning from only local information,” Neural Comput . 10(8), 2047–2084 (1998).CrossRefGoogle Scholar
Kuffner, J. J., “Effective Sampling and Distance Metrics for 3D Rigid Body Path Planning,” In: Proceedings of the 2004 IEEE International Conference on Robotics and Automation, ICRA 2004, New Orleans, LA (2004) pp. 3993–3998.Google Scholar