Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Autonomous Robots
Published in: Autonomous Robots 5/2018

06-11-2017

Robot adaptation to human physical fatigue in human–robot co-manipulation

Authors: Luka Peternel, Nikos Tsagarakis, Darwin Caldwell, Arash Ajoudani

Published in: Autonomous Robots | Issue 5/2018

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

In this paper, we propose a novel method for human–robot collaboration, where the robot physical behaviour is adapted online to the human motor fatigue. The robot starts as a follower and imitates the human. As the collaborative task is performed under the human lead, the robot gradually learns the parameters and trajectories related to the task execution. In the meantime, the robot monitors the human fatigue during the task production. When a predefined level of fatigue is indicated, the robot uses the learnt skill to take over physically demanding aspects of the task and lets the human recover some of the strength. The human remains present to perform aspects of collaborative task that the robot cannot fully take over and maintains the overall supervision. The robot adaptation system is based on the Dynamical Movement Primitives, Locally Weighted Regression and Adaptive Frequency Oscillators. The estimation of the human motor fatigue is carried out using a proposed online model, which is based on the human muscle activity measured by the electromyography. We demonstrate the proposed approach with experiments on real-world co-manipulation tasks: material sawing and surface polishing.
previous article Skill-based human–robot cooperation in tele-operated path tracking
next article Human robot cooperation with compliance adaptation along the motion trajectory

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now
Appendix
Available only for authorised users
Footnotes
1
When the robot interacts with the rough environment the force measurement can be noisy (Peternel et al. 2014, 2017). Therefore, we did not use derivative term in the controller to avoid stability issues.
 
2
Note that this fatigue estimation procedure is subject-dependant. The subject was asked to endure the effort until task production became uncomfortable due to the muscle fatigue.
 
3
MVC calibration should be ideally performed every time the electrodes are reattached. However, the calibration of endurance time related parameter should theoretically be reusable if no drastic changes are made (e.g., muscle endurance may improve through physical training, etc.).
 
4
The value represents the mean and standard deviation of data from all subjects across the measured samples in the given stage of the experiment.
 
5
If required, the proposed human–robot interface could be extended to include voice command that can be used by the human to indicate to the robot to increase the stiffness in the y–z plane to maintain some desired position. However, the information flow rate of voice command is much lower compared to that of muscle activity interface (7) and could therefore be used only for auxiliary robot stiffness control.
 
Literature
Agravante, D., Cherubini, A., Bussy, A., Gergondet, P., & Kheddar, A. (2014). Collaborative human-humanoid carrying using vision and haptic sensing. In Robotics and Automation (ICRA), 2014 IEEE International Conferene on (pp. 607–612).
Ajoudani, A. (2016). Transferring human impedance regulation skills to robots. Berlin: Springer.CrossRef
Ajoudani, A., Godfrey, S., Bianchi, M., Catalano, M., Grioli, G., Tsagarakis, N., et al. (2014). Exploring teleimpedance and tactile feedback for intuitive control of the pisa/iit softhand. IEEE Transactions on Haptics, 7(2), 203–215.CrossRef
Albu-Schäffer, A., Haddadin, S., Ott, C., Stemmer, A., Wimböck, T., & Hirzinger, G. (2007). The DLR lightweight robot: Design and control concepts for robots in human environments. Industrial Robot: An International Journal, 34(5), 376–385.CrossRef
Albu-Schäffer, A., Ott, C., Frese, U., & Hirzinger, G. (2003). Cartesian impedance control of redundant robots: recent results with the DLR-light-weight-arms. In Robotics and Automation (ICRA), 2003 IEEE International Conference on (vol. 3, pp. 3704–3709).
Ben Amor, H., Neumann, G., Kamthe, S., Kroemer, O., & Peters, J. (2014). Interaction primitives for human–robot cooperation tasks. In Robotics and Automation (ICRA), 2014 IEEE International Conference on (pp. 2831–2837).
Burdet, E., Osu, R., Franklin, D. W., Milner, T. E., & Kawato, M. (2001). The central nervous system stabilizes unstable dynamics by learning optimal impedance. Nature, 414(6862), 446–449.CrossRef
De Luca, C. J. (1984). Myoelectrical manifestations of localized muscular fatigue in humans. Critical Reviews in Biomedical Engineering, 11(4), 251–279.
Ding, J., Wexler, A. S., & Binder-Macleod, S. A. (2000). A predictive model of fatigue in human skeletal muscles. Journal of Applied Physiology, 89(4), 1322–1332.CrossRef
Donner, P., & Buss, M. (2016). Cooperative swinging of complex pendulum-like objects: Experimental evaluation. IEEE Transactions on Robotics, 32(3), 744–753.CrossRef
Dragan, A. D., & Srinivasa, S. S. (2013). A policy-blending formalism for shared control. The International Journal of Robotics Research, 32(7), 790–805.CrossRef
Enoka, R. M., & Duchateau, J. (2008). Muscle fatigue: What, why and how it influences muscle function. The Journal of physiology, 586(1), 11–23.CrossRef
Evrard, P., Gribovskaya, E., Calinon, S., Billard, A., & Kheddar, A. (2009). Teaching physical collaborative tasks: Object-lifting case study with a humanoid. In IEEE-RAS International Conference on Humanoid Robots (pp. 399–404).
Fleischer, C., & Hommel, G. (2008). A human-exoskeleton interface utilizing electromyography. IEEE Transactions on Robotics, 24(4), 872–882.CrossRef
Giat, Y., Mizrahi, J., & Levy, M. (1993). A musculotendon model of the fatigue profiles of paralyzed quadriceps muscle under fes. IEEE Transactions on Biomedical Engineering, 40(7), 664–674.CrossRef
Gribovskaya, E., Kheddar, A., & Billard, A. (2011). Motion learning and adaptive impedance for robot control during physical interaction with humans. In Robotics and Automation (ICRA), 2011 IEEE International Conference on (pp. 4326–4332).
Hogan, N. (1984). Adaptive control of mechanical impedance by coactivation of antagonist muscles. IEEE Transactions on Automatic Control, 29(8), 681–690.CrossRefMATH
Ijspeert, A. J., Nakanishi, J., & Schaal, S. (2003). Learning attractor landscapes for learning motor primitives. In S. Becker, S. Thrun, & K. Obermayer (Eds.), Advances in neural information processing systems (pp. 1523–1530). Cambridge, MA: MIT Press.
Ikemoto, S., Ben Amor, H., Minato, T., Jung, B., & Ishiguro, H. (2012). Physical human–robot interaction: Mutual learning and adaptation. IEEE Robotics Automation Magazine, 19(4), 24–35.CrossRef
Ikeura, R., & Inooka, H. (1995). Variable impedance control of a robot for cooperation with a human. In Robotics and Automation (ICRA), 1995 IEEE International Conference on (vol. 3, pp. 3097–3102).
Kaneko, K., Harada, K., Kanehiro, F., Miyamori, G., & Akachi, K. (2008). Humanoid robot HRP-3. In Intelligent Robots and Systems (IROS), 2008 IEEE/RSJ International Conference on (pp. 2471–2478).
Kosuge, K. & Kazamura, N. (1997). Control of a robot handling an object in cooperation with a human. In Robot and Human Communication, 6th IEEE International Workshop on (pp. 142–147).
Lee, D., & Ott, C. (2011). Incremental kinesthetic teaching of motion primitives using the motion refinement tube. Autonomous Robots, 31(2), 115–131.CrossRef
Liu, J. Z., Brown, R. W., & Yue, G. H. (2002). A dynamical model of muscle activation, fatigue, and recovery. Biophysical Journal, 82(5), 2344–2359.CrossRef
Ma, L., Chablat, D., Bennis, F., & Zhang, W. (2009). A new simple dynamic muscle fatigue model and its validation. International Journal of Industrial Ergonomics, 39(1), 211–220.CrossRef
Ma, L., Chablat, D., Bennis, F., Zhang, W., & Guillaume, F. (2010). A new muscle fatigue and recovery model and its ergonomics application in human simulation. Virtual and Physical Prototyping, 5(3), 123–137.CrossRef
Maeda, G. J., Neumann, G., Ewerton, M., Lioutikov, R., Kroemer, O., & Peters, J. (2017). Probabilistic movement primitives for coordination of multiple human–robot collaborative tasks. Autonomous Robots, 41(3), 593–612.CrossRef
Medina, J., Shelley, M., Lee, D., Takano, W., & Hirche, S. (2012). Towards interactive physical robotic assistance: Parameterizing motion primitives through natural language. In RO-MAN, 2012 IEEE (pp. 1097–1102).
Nikolaidis, S., Kuznetsov, A., Hsu, D., & Srinivasa, S. (2016). Formalizing human–robot mutual adaptation: A bounded memory model. In 2016 11th ACM/IEEE International Conference on Human–Robot Interaction (HRI), (pp. 75–82).
Peternel, L., & Babič, J. (2013). Learning of compliant human–robot interaction using full-body haptic interface. Advanced Robotics, 27(13), 1003–1012.CrossRef
Peternel, L., Noda, T., Petrič, T., Ude, A., Morimoto, J., & Babič, J. (2016a). Adaptive control of exoskeleton robots for periodic assistive behaviours based on EMG feedback minimisation. PLoS ONE, 11(2), e0148942.CrossRef
Peternel, L., Oztop, E., & Babič, J. (2016b). A shared control method for online human-in-the-loop robot learning based on locally weighted regression. In 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), (pp. 3900–3906).
Peternel, L., Petrič, T., Oztop, E., & Babič, J. (2014). Teaching robots to cooperate with humans in dynamic manipulation tasks based on multi-modal human-in-the-loop approach. Autonomous Robots, 36(1–2), 123–136.CrossRef
Peternel, L., Tsagarakis, N., & Ajoudani, A. (2017). A human-robot co-manipulation approach based on human sensorimotor information. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 25(7), 811–822.CrossRef
Peternel, L., Tsagarakis, N., Caldwell, D., & Ajoudani, A. (2016c). Adaptation of robot physical behaviour to human fatigue in human–robot co-manipulation. In IEEE-RAS International Conference on Humanoid Robots (pp. 489–494).
Petrič, T., Gams, A., Ijspeert, A. J., & Žlajpah, L. (2011). On-line frequency adaptation and movement imitation for rhythmic robotic tasks. The International Journal of Robotics Research, 30(14), 1775–1788.CrossRef
Rozo, L., Bruno, D., Calinon, S., Caldwell, D. G. (2015). Learning optimal controllers in human–robot cooperative transportation tasks with position and force constraints. In Intelligent Robots and Systems (IROS), 2015 IEEE/RSJ International Conference on.
Sadrfaridpour, B., Saeidi, H., Burke, J., Madathil, K., & Wang, Y. (2016). Modeling and control of trust in human–robot collaborative manufacturing (pp. 115–141). Boston, MA.: Springer.
Schaal, S., & Atkeson, C. G. (1998). Constructive incremental learning from only local information. Neural Computation, 10(8), 2047–2084.CrossRef
Tsagarakis, N., Caldwell, D. G., Bicchi, A., Negrello, F., Garabini, M., Choi, W., et al. (2017). WALK-MAN: A high performance humanoid platform for realistic environments. Journal of Field Robotics, 34(7), 1225–1259.
Tsumugiwa, T., Yokogawa, R., & Hara, K. (2002). Variable impedance control based on estimation of human arm stiffness for human–robot cooperative calligraphic task. In Robotics and Automation (ICRA), 2002 IEEE International Conference on (vol. 1, pp. 644–650).
Turvey, M. (2007). Action and perception at the level of synergies. Human Movement Science, 26(4), 657–697.CrossRef
Metadata
Title
Robot adaptation to human physical fatigue in human–robot co-manipulation
Authors
Luka Peternel
Nikos Tsagarakis
Darwin Caldwell
Arash Ajoudani
Publication date
06-11-2017
Publisher
Springer US
Published in
Autonomous Robots / Issue 5/2018
Print ISSN: 0929-5593
Electronic ISSN: 1573-7527
DOI
https://doi.org/10.1007/s10514-017-9678-1

Other articles of this Issue 5/2018

Autonomous Robots 5/2018 Go to the issue

OriginalPaper

Learning proactive behavior for interactive social robots

OriginalPaper

Co-manipulation with a library of virtual guiding fixtures

OriginalPaper

Progress and prospects of the human–robot collaboration

OriginalPaper

Human robot cooperation with compliance adaptation along the motion trajectory

OriginalPaper

One-shot learning of human–robot handovers with triadic interaction meshes

OriginalPaper

Hierarchical emotional episodic memory for social human robot collaboration