Autonomous Robots

, Volume 41, Issue 2, pp 333–347 | Cite as

Learning autonomous behaviours for the body of a flexible surgical robot

  • Danilo Bruno
  • Sylvain Calinon
  • Darwin G. Caldwell


This paper presents a novel strategy to learn a positional controller for the body of a flexible surgical manipulator used for minimally invasive surgery. The manipulator is developed within the STIFF-FLOP European project and is targeted for a laparoscopic use in remote areas of the abdominal region that are not easily accessible by means of currently available rigid tools. While the surgeon controls the end-effector during the task, the flexible body of the manipulator needs to be displaced to enter inside constrained spaces by efficiently exploiting its flexibility, without touching vital organs and structures. The proposed algorithm exploits the instruments of machine learning within the programming by demonstrations paradigm to produce a statistical model of the natural movements of the surgeon during the task. The gathered information is then reused to determine a controller in the null space of the robot that does not interfere with the surgeon task and displaces the robot body within the available space in a fully automated manner.


Learning from demonstrations Surgical robotics Online learning Nullspace control 



This work was partially supported by the STIFF-FLOP European project under contract FP7-ICT-287728.


  1. Bruno, D., Calinon, S., & Caldwell, D. G. (2014). Null space redundancy learning for a flexible surgical robot. In IEEE international conference on robotics and automation (ICRA) (pp. 2443–2448). Hong Kong.Google Scholar
  2. Calinon, S., Bruno, D., & Caldwell, D. G. (2014a). A task-parameterized probabilistic model with minimal intervention control. In IEEE international conference on robotics and automation (ICRA) (pp. 3339–3344). Hong Kong.Google Scholar
  3. Calinon, S., Bruno, D., Malekzadeh, M. S., Nanayakkara, T., & Caldwell, D. G. (2014b). Human-robot skills transfer interfaces for a flexible surgical robot. Computer Methods and Programs in Biomedicine, 116(2), 81–96. Special issue on new methods of human-robot interaction in medical practice.Google Scholar
  4. Calinon, S., D’halluin, F., Sauser, E. L., Caldwell, D. G., & Billard, A. G. (2010). Learning and reproduction of gestures by imitation: An approach based on hidden Markov model and Gaussian mixture regression. IEEE Robotics and Automation Magazine, 17(2), 44–54.CrossRefGoogle Scholar
  5. Cianchetti, M., Ranzani, T., Gerboni, G., De Falco, I., Laschi, C., & Menciassi, A. (2013). Stiff-flop surgical manipulator: mechanical design and experimental characterization of the single module. In IEEE/RSJ international conference on intelligent robots and systems (IROS) (pp. 3567–3581).Google Scholar
  6. Flash, T., & Hogan, N. (1985). The coordination of the arm movements: An experimentally confirmed mathematical model. Neurology, 5(7), 1688–1703.Google Scholar
  7. Gauvain, J.-L., & Lee, C.-H. (1994). Maximum a posteriori estimation for multivariate gaussian mixture observations of markov chians. IEE Transactions on Speech and Audio Processing, 2(2), 291–298.CrossRefGoogle Scholar
  8. Gielniak, M. J., Liu, C. K., & Thomaz, A. L. (2011). Task-aware variations in robot motion. In IEEE international conference on robotics and automation (ICRA) (pp. 3921–3927).Google Scholar
  9. Jiang, A., Ataollahi, A., Althoefer, K., Dasgupta, P., & Nanayakkara, T. (2012a). A variable stiffness joint by granular jamming. In ASME international design engineering technical conference and computers and information in engineering conference (IDETC/CIE) (pp. 267–275).Google Scholar
  10. Jiang, A., Xynogalas, G., Dasgupta, P., Althoefer, K., & Nanayakkara, T. (2012b). Design of a variable stiffness flexible manipulator with composite granular jamming and membrane coupling. In IEEE/RSJ international conference on intelligent robots and systems (IROS) (pp. 2922–2927).Google Scholar
  11. Kulis, B., & Jordan, M. I. (2012). Revisiting k-means: New algorithms via bayesian nonparametrics. In Proceedings of the international conference on machine learning (ICML), Edimburgh.Google Scholar
  12. Lee, D., & Ott, C. (2011). Incremental kinesthetic teaching of motion primitives using the motion refinement tube. Autonomous Robots, 31(2), 115–131.CrossRefGoogle Scholar
  13. Lobaton, E., Fu, J., Torres, L., & Alterovitz, R. (2013). Continuous shape estimation of continuum robots using x-ray images. In IEEE international conference on robotics and automation (ICRA) (pp. 725–732).Google Scholar
  14. Lyons, L., Webster, R., & Alterovitz, R. (2010). Planning active cannula configurations through tubular anatomy. In IEEE international conference on robotics and automation (ICRA) (pp. 2082–2087). Anchorage, AK.Google Scholar
  15. MacQueen, J. B. (1967). Some methods for classification and analysis of multivariate observations. In Proceedings of the symposium on mathematical statistics and probability (pp. 281–297), Berkeley: University of California Press.Google Scholar
  16. Malekzadeh, M. S., Calinon, S., Bruno, D., & Caldwell, D. G. (2014). Learning by imitation with the STIFF-FLOP surgical robot: A biomimetic approach inspired by octopus movements. Robotics and Biomimetics Special Issue on Medical Robotics, 1(13), 1–15.Google Scholar
  17. Nordmann, A., Emmerich, C., Ruether, S., Lemme, A., Wrede, S., & Steil, J. (2012). Teaching nullspace constraints in physical human-robot interaction using reservoir computing. In IEEE international conference on robotics and automation (ICRA) (pp. 1868–1875).Google Scholar
  18. Quinlan, S., & Khatib, O. (1993). Elastic bands: Connecting path planning and control. In Proceedings of the international conference on robotics and automation (ICRA) (pp. 802–807).Google Scholar
  19. Rajiv Ranganatan, A. A., & Mussa-Ivaldi, F. A. (2013). Learning to be lazy, exploiting redundancy in a novel task to minimize movement-related effort. Journal of Neuroscience, 33(7), 2754–2760.CrossRefGoogle Scholar
  20. Reiter, A., Goldman, R. E., Bajo, A., Iliopoulos, K., Simaan, N., & Allen, P. K. (2011). A learning algorithm for visual pose estimation of continuum robots. In IEEE international conference on robotics and automation (ICRA) (pp. 2390–2396), San Francisco, CA.Google Scholar
  21. Song, M., & Wang, H. (2005). Highly efficient incremental estimation of Gaussian mixture models for online data stream clustering. In Proceedings of SPIE: Intelligent computing—theory and applications III (Vol. 5803, pp. 174–183).Google Scholar
  22. Sternad, D., Abe, M. O., Hu, X., & Mueller, H. (2011). Neuromotor noise, error tolerance and velocity-dependent costs in skilled performance. PLoS Computational Biology, 7(9), e1002159.CrossRefGoogle Scholar
  23. Todorov, E., & Jordan, M. I. (2002). A minimal intervention principle for coordinated movement. In Advances in neural information processing systems (NIPS) (pp. 27–34).Google Scholar
  24. Towell, C., Howard, M., & Vijayakumar, S. (2010). Learning nullspace policies. In Proceedings of IEEE/RSJ international conference on intelligent robots and systems (IROS) (pp. 241–248).Google Scholar
  25. Van Den Berg, J., Miller, S., Duckworth, D., Hu, H., Wan, A., Fu, X., et al. (2010). Superhuman performance of surgical tasks by robots using iterative learning from human-guided demonstrations. In IEEE international conference on robotics and automation (ICRA) (pp. 2074–2081). Anchorage, AK.Google Scholar
  26. Yang, X., Zhu, C., Yang, M., Hu, H., Xia, L., & Pan, K. (2012). Laparoscopic radical resection for rectal cancer. Translational Gastrointestinal Cancer, 1(3), 255–271.Google Scholar
  27. Zelman, I., Titon, M., Yekutieli, Y., Hanassy, S., Hochner, B., & Flash, T. (2013). Kinematic decomposition and classification of octopus arm movements. Frontiers in Computational Neuroscience, 7, 60.CrossRefGoogle Scholar
  28. Zhang, Z., Chen, C., Sun, J., & Chan, K. L. (2003). EM algorithms for Gaussian mixtures with split-and-merge operation. Pattern Recognition, 36(9), 1973–1983.CrossRefzbMATHGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Danilo Bruno
    • 1
  • Sylvain Calinon
    • 1
    • 2
  • Darwin G. Caldwell
    • 1
  1. 1.Department of Advanced RoboticsIstituto Italiano di Tecnologia (IIT)GenoaItaly
  2. 2.Idiap Research InstituteMartignySwitzerland

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