Advertisement

Human robot cooperation with compliance adaptation along the motion trajectory

  • 987 Accesses

  • 7 Citations

Abstract

In this paper we propose a novel approach for intuitive and natural physical human–robot interaction in cooperative tasks. Through initial learning by demonstration, robot behavior naturally evolves into a cooperative task, where the human co-worker is allowed to modify both the spatial course of motion as well as the speed of execution at any stage. The main feature of the proposed adaptation scheme is that the robot adjusts its stiffness in path operational space, defined with a Frenet–Serret frame. Furthermore, the required dynamic capabilities of the robot are obtained by decoupling the robot dynamics in operational space, which is attached to the desired trajectory. Speed-scaled dynamic motion primitives are applied for the underlying task representation. The combination allows a human co-worker in a cooperative task to be less precise in parts of the task that require high precision, as the precision aspect is learned and provided by the robot. The user can also freely change the speed and/or the trajectory by simply applying force to the robot. The proposed scheme was experimentally validated on three illustrative tasks. The first task demonstrates novel two-stage learning by demonstration, where the spatial part of the trajectory is demonstrated independently from the velocity part. The second task shows how parts of the trajectory can be rapidly and significantly changed in one execution. The final experiment shows two Kuka LWR-4 robots in a bi-manual setting cooperating with a human while carrying an object.

This is a preview of subscription content, log in to check access.

Access options

Buy single article

Instant unlimited access to the full article PDF.

US$ 39.95

Price includes VAT for USA

Subscribe to journal

Immediate online access to all issues from 2019. Subscription will auto renew annually.

US$ 99

This is the net price. Taxes to be calculated in checkout.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

References

  1. Adorno, B. V., Bó A. P. L., Fraisse, P., & Poignet, P. (2011). Towards a cooperative framework for interactive manipulation involving a human and a humanoid. In International conference on robotics and automation (ICRA) (pp. 3777–3783).

  2. Adorno, B., Fraisse, P., & Druon, S. (2010). Dual position control strategies using the cooperative dual task-space framework. In IEEE/RSJ international conference on intelligent robots and systems (IROS), Taipei, Taiwan (pp. 3955–3960).

  3. Albu-Schaffer, A., Ott, C., & Hirzinger, G. (2004). A passivity based Cartesian impedance controller for flexible joint robots—part II: Full state feedback, impedance design and experiments. In IEEE international conference on robotics and automation, 2004 proceedings ICRA ’04 2004 3(5) (pp. 2659–2665).

  4. Albu-Schaffer, A., Ott, C., & Hirzinger, G. (2007). A unified passivity-based control framework for position, torque and impedance control of flexible joint robots. The International Journal of Robotics Research, 26(1), 23–39.

  5. Amor, H. B., Neumann, G., Kamthe, S., Kroemer, O., & Peters, J. (2014). Interaction primitives for human-robot cooperation tasks. In 2014 IEEE international conference on robotics and automation (ICRA) (pp. 2831–2837).

  6. Caccavale, F., Chiacchio, P., & Chiaverini, S. (2000). Task-space regulation of cooperative manipulators. Automatica, 36, 879–887.

  7. Calinon, S., Bruno, D., & Caldwell, D. G. (2014). A task-parameterized probabilistic model with minimal intervention control. In IEEE international conference on robotics and automation (ICRA), Hong Kong (pp. 3339–3344).

  8. Calinon, S., Li, Z., Alizadeh, T., Tsagarakis, N. G., & Caldwell, D. G. (2012). Statistical dynamical systems for skills acquisition in humanoids. In 12th IEEE-RAS international conference on humanoid robots (humanoids), Osaka, Japan (pp. 323–329).

  9. Calinon, S., Sardellitti, I., & Caldwell, D. G. (2010). Learning-based control strategy for safe human-robot interaction exploiting task and robot redundancies. In IEEE/RSJ international conference on intelligent robots and systems (IROS) (pp. 249–254).

  10. Chiaverini, S., Oriolo, G., & Walker, I. D. (2008). Chapter 11: Kinematically redundant manipulators. Springer Handbook of Robotics (pp. 245–268). Berlin Heidelberg: Springer.

  11. Evrard, P., Mansard, N., Stasse, O., Kheddar, A., Schauss, T., Weber, C., Peer, A., & Buss, M. (2009). Intercontinental, multimodal, wide-range telecooperation using a humanoid robot. In IEEE/RSJ International conference on intelligent robots and systems (IROS) (pp. 5635–5640).

  12. Ewerton, M., Neumann, G., Lioutikov, R., Amor, H. B., Peters, J., & Maeda, G. (2015). Learning multiple collaborative tasks with a mixture of interaction primitives. In 2015 IEEE International Conference on Robotics and Automation (ICRA) (pp. 1535–1542).

  13. Faber, M., Bützler, J., & Schlick, C. M. (2015). Human–robot cooperation in future production systems: Analysis of requirements for designing an ergonomic work system. Procedia Manufacturing, 3, 510–517.

  14. Fitts, P. M. (1954). The information capacity of the human motor system in controlling the amplitude of movement. Journal of Experimental Psychology, 47(6), 381–391.

  15. Gams, A., Nemec, B., Ijspeert, A. J., & Ude, A. (2014). Coupling movement primitives: Interaction with the environment and bimanual tasks. IEEE Transactions on Robotics, 30(4), 816–830.

  16. Gams, A., Petrič, T., Do, M., Nemec, B., Morimoto, J., Asfour, T., et al. (2016). Adaptation and coaching of periodic motion primitives through physical and visual interaction. Robotics and Autonomous Systems, 75(Part B), 340–351.

  17. Hatanaka, T., Chopra, N., & Spong, M. W. (2015). Passivity-based control of robots: Historical perspective and contemporary issues. In Conference on decision and control (CDC) (pp. 2450–2452).

  18. Ijspeert, A. J., Nakanishi, J., Hoffmann, H., Pastor, P., & Schaal, S. (2013). Dynamical movement primitives: Learning attractor models for motor behaviors. Neural Computation, 25(2), 328–73.

  19. Khansari-Zadeh, S. M., & Billard, A. (2011). Learning stable nonlinear dynamical systems with gaussian mixture models. IEEE Transactions on Robotics, 27(5), 943–957.

  20. Khatib, O. (1987). A unified approach for motion and force control of robot manipulators: The operational space formulation. IEEE Journal of Robotics and Automation, 3, 43–53.

  21. Knuth, D. E. (1997). The Art of computer programming, (3rd ed., Vol. 2). Inc, Boston, MA, USA: Seminumerical Algorithms. Addison-Wesley Longman Publishing Co.

  22. Krebs, H. I., Hogan, N., Aisen, M. L., & Volpe, B. T. (1998). Robot-aided neurorehabilitation. IEEE Transactions on Rehabilitation Engineering, 6(December), 75–87.

  23. Likar, N., Nemec, B., Zlajpah, L., Ando, S., & Ude, A. (2015). Adaptation of bimanual assembly tasks using iterative learning framework. IEEE-RAS international conference on humanoid robots (humanoids), Seoul, Korea (pp. 771–776).

  24. Mortl, A., Lawitzky, M., Kucukyilmaz, A., Sezgin, M., Basdogan, C., & Hirche, S. (2012). The role of roles: Physical cooperation between humans and robots. The International Journal of Robotics Research, 31(13), 1656–1674.

  25. Nemec, B., Gams, A., & Ude, A. (2013). Velocity adaptation for self-improvement of skills learned from user demonstrations. IEEE-RAS International conference on humanoid robots (humanoids), Atlanta, USA (pp. 423–428).

  26. Nemec, B., Likar, N., Gams, A., & Ude, A. (2016a). Adaptive human robot cooperation scheme for bimanual robots. In J. Lenarcic & J. Merlet (Eds.), Advances in robot kinematics (pp. 385–393). Rocquencourt: INRIA.

  27. Nemec, B., Likar, N., Gams, A., & Ude, A. (2016b) Bimanual human robot cooperation with adaptive stiffness control. In 16th IEEE-RAS International Conference on Humanoid Robots, Cancun, Mexico (pp. 607–613).

  28. Ott, C., Albu-Schaffer, A., Kugi, A., Stramigioli, S., & Hirzinger, G. (2004). A passivity based cartesian impedance controller for flexible joint robots-part I: Torque feedback and gravity compensation. In IEEE international conference on robotics & automation (pp. 2659–2665).

  29. Paraschos, A., Daniel, C., Peters, J., & Neumann, G. (2013). Probabilistic movement primitives. Neural Information Processing Systems, 26, 2616–2624.

  30. Park, H. A., & Lee, C. S. G. (2015). Extended cooperative task space for manipulation tasks of humanoid robots. In IEEE international conference on robotics and automation (ICRA), Seattle, WA (pp. 6088–6093).

  31. Raiola, G., Lamy, X., & Stulp, F. (2015). Co-manipulation with multiple probabilistic virtual guides. In 2015 IEEE/RSJ international conference on intelligent robots and systems (IROS) (pp. 7–13).

  32. Ramacciotti, M., Milazzo, M., Leoni, F., Roccella, S., & Stefanini, C. (2016). A novel shared control algorithm for industrial robots. International Journal of Advanced Robotic Systems, 13(6), 1729881416682701. https://doi.org/10.1177/1729881416682701.

  33. Ravani, R., & Meghdari, A. (2006). Velocity distribution profile for robot arm motion using rational Frenet–Serret curves. Informatica, 17(1), 69–84.

  34. Rozo, L., Calinon, S., Caldwell, D. G., Jimnez, P., & Torras, C. (2016). Learning physical collaborative robot behaviors from human demonstrations. IEEE Transactions on Robotics, 32(3), 513–527.

  35. Salehian, S. S. M., Khoramshahi, M., & Billard, A. (2016). A dynamical system approach for softly catching a flying object: Theory and experiment. IEEE Transactions on Robotics, 32(2), 462–471.

  36. Soler, T., & Chin, M. (1985). On transformation of covariance matrices between local cartesian coordinate systems and commutative diagrams. In Proceedings of 45th Annual Meeting ACSM-ACSM Convention (pp. 393–406).

  37. Soyama, R., Ishii, S., & Fukase, A. (2004). Selectable operating interfaces of the meal-assistance device “my spoon”. In Z. Bien & D. Stefanov (Eds.), Rehabilitation (pp. 155–163). Berlin: Springer.

  38. Ude, A., Gams, A., Asfour, T., & Morimoto, J. (2010). Task-specific generalization of discrete and periodic dynamic movement primitives. IEEE Transactions on Robotics, 26(5), 800–815.

  39. Ude, A., Nemec, B., Petrič, T., & Morimoto, J. (2014). Orientation in cartesian space dynamic movement primitives. IEEE international conference on robotics and automation (ICRA), Hong Kong, China (pp. 2997–3004).

  40. Zhang, J., & Cheah, C. C. (2015). Passivity and stability of human–robot interaction control for upper-limb rehabilitation robots. IEEE Transactions on Robotics, 31(2), 233–245.

Download references

Acknowledgements

The research leading to these results has received partial funding from the Horizon 2020 FoF Research & Innovation Action no. 723909, AUTOWARE, and by the GOSTOP programme, contract no C3330-16-529000, co-financed by Slovenia and the EU under the ERDF.

Author information

Correspondence to Bojan Nemec.

Additional information

This is one of the several papers published in Autonomous Robots comprising the Special Issue on Learning for Human-Robot Collaboration.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (mp4 3448 KB)

Supplementary material 2 (mp4 9127 KB)

Supplementary material 1 (mp4 3448 KB)

Supplementary material 2 (mp4 9127 KB)

Supplementary material 3 (mp4 4189 KB)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Nemec, B., Likar, N., Gams, A. et al. Human robot cooperation with compliance adaptation along the motion trajectory. Auton Robot 42, 1023–1035 (2018). https://doi.org/10.1007/s10514-017-9676-3

Download citation

Keywords

  • Human robot coordination
  • Learning by demonstration
  • Dynamic motion primitives
  • Robot learning
  • Robot control