Application and Exploration of Sensorimotor Coordination Strategies in Surgical Robotics

  • Anthony Jarc
  • Ilana NiskyEmail author
Part of the Cognitive Systems Monographs book series (COSMOS, volume 36)


Robot-assisted minimally invasive surgery (RAMIS) is a highly complex sensorimotor task. The architecture of current RAMIS platforms enables surgeons to use master manipulators to precisely and intuitively control surgical instruments to complete intricate procedures. However, a comprehensive understanding of surgeon sensorimotor behavior is lacking. In this chapter, we discuss a research avenue that seeks to improve RAMIS by applying ideas from basic science and, in turn, to further develop these ideas to improve our fundamental understanding of human sensorimotor coordination. We discuss why RAMIS could serve as an excellent research platform, as well as what general assumptions are made when applying theories to RAMIS. In the end, we believe that RAMIS provides an exciting opportunity for integrated research in robotics and sensorimotor behavior.



The authors wish to thank Myriam Curet for her valuable comments on the manuscript. IN was funded by the Marie Curie International Outgoing Fellowship, and the Weizmann Institute of Science National Postdoctoral Award for Advancement of Women in Science.


  1. 1.
    Abboudi, H., Khan, M.S., Aboumarzouk, O., Guru, K.A., Challacombe, B., Dasgupta, P., Ahmed, K.: Current status of validation for robotic surgery simulators–a systematic review. BJU Int. (2012)Google Scholar
  2. 2.
    Ahmidi, N., Hager, G., Ishii, L., Fichtinger, G., Gallia, G., Ishii, M.: Surgical task and skill classification from eye tracking and tool motion in minimally invasive surgery. In: Jiang, T. et al. (eds.) Medical Image Computing and Computer-Assisted Intervention—MICCAI 2010, vol. 6363, pp 295–302. Springer Berlin Heidelberg (2010)Google Scholar
  3. 3.
    Albert, M.V., Kording, K., Herrmann, M., Jayaraman, A.: Fall classification by machine learning using mobile phones. PLoS ONE 7, e36556 (2012)CrossRefGoogle Scholar
  4. 4.
    Albert, M.V., Toledo, S., Shapiro, M., Kording, K.: Using mobile phones for activity recognition in Parkinson’s patients. Front. Neurol. 3 (2012)Google Scholar
  5. 5.
    Amodeo, A., Linares Quevedo, A., Joseph, J.V., Belgrano, E., Patel, H.R.: Robotic laparoscopic surgery: cost and training. Minerva Urol. Nefrol. 61, 121–128 (2009)Google Scholar
  6. 6.
    Antos, S.A., Albert, M.V., Kording, K.P.: Hand, belt, pocket or bag: practical activity tracking with mobile phones. J. Neurosci. Methods (2013)Google Scholar
  7. 7.
    Avraham, G., Nisky, I., Fernandes, H., Acuna, D., Kording, K., Loeb, G., Karniel, A.: Towards perceiving robots as humans—three handshake models face the turing-like handshake test. IEEE Trans. Haptics 5, 196–207 (2012)CrossRefGoogle Scholar
  8. 8.
    Balasubramanian, R., Howe, R.D., Matsuoka, Y.: Task performance is prioritized over energy reduction. IEEE Trans. Biomed. Eng. 56, 1310–1317 (2009)CrossRefGoogle Scholar
  9. 9.
    Ballantyne, G.H.: The pitfalls of laparoscopic surgery: challenges for robotics and telerobotic surgery. Surg. Laparosc. Endosc. Percutaneous Tech. 12, 1–5 (2002)CrossRefGoogle Scholar
  10. 10.
    Ben-Itzhak, S., Karniel, A.: Minimum acceleration criterion with constraints implies bang-bang control as an underlying principle for optimal trajectories of arm reaching movements. Neural Comput. 20, 779–812 (2008)MathSciNetzbMATHCrossRefGoogle Scholar
  11. 11.
    Berniker, M., Jarc, A., Bizzi, E., Tresch, M.C.: Simplified and effective motor control based on muscle synergies to exploit musculoskeletal dynamics. Proc. Natl. Acad. Sci. 106, 7601–7606 (2009)CrossRefGoogle Scholar
  12. 12.
    Berniker, M., Kording, K.P.: Estimating the relevance of world disturbances to explain savings, interference and long-term motor adaptation effects. PLoS Comput. Biol. 7, e1002210 (2011)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Bernstein, N.: The Coordination and Regulation of Movements. Pergamon Press, Oxford (1967)Google Scholar
  14. 14.
    Brayanov, J.B., Smith, M.A.: Bayesian and “Anti-Bayesian” biases in sensory integration for action and perception in the size-weight illusion. J. Neurophysiol. 103, 1518–1531 (2010)CrossRefGoogle Scholar
  15. 15.
    Burdet, E., Franklin, D.W., Milner, T.E.: Human Robotics: Neuromechanics and Motor Control. MIT Press (2013)Google Scholar
  16. 16.
    Burdet, E., Osu, R., Franklin, D.W., Milner, T.E., Kawato, M.: The central nervous system stabilizes unstable dynamics by learning optimal impedance. Nature 414, 446–449 (2001)CrossRefGoogle Scholar
  17. 17.
    Carey, D.P.: Do action systems resist visual illusions? Trends Cogn. Sci. 5, 109–113 (2001)CrossRefGoogle Scholar
  18. 18.
    Casadio, M., Giannoni, P., Morasso, P., Sanguineti, V.: A proof of concept study for the integration of robot therapy with physiotherapy in the treatment of stroke patients. Clin. Rehabil. 23, 217–228 (2009)CrossRefGoogle Scholar
  19. 19.
    Chang, L., Satava, R., Pellegrini, C., Sinanan, M.: Robotic surgery: identifying the learning curve through objective measurement of skill. Surg. Endosc. Other Interv. Tech. 17, 1744–1748 (2003)CrossRefGoogle Scholar
  20. 20.
    Cohen, R., Sternad, D.: Variability in motor learning: relocating, channeling and reducing noise. Exp. Brain Res. 193, 69–83 (2009)CrossRefGoogle Scholar
  21. 21.
    Cusumano, J., Cesari, P.: Body-goal variability mapping in an aiming task. Biol. Cybern. 94, 367–379 (2006)MathSciNetzbMATHCrossRefGoogle Scholar
  22. 22.
    da Vinci Research Kit (2013)Google Scholar
  23. 23.
    Danion, F., Diamond, J.S., Flanagan, J.R.: Separate contributions of kinematic and kinetic errors to trajectory and grip force adaptation when transporting novel hand-held loads. J. Neurosci. 33, 2229–2236 (2013)CrossRefGoogle Scholar
  24. 24.
    Desmurget, M., Jordan, M., Prablanc, C., Jeannerod, M.: Constrained and Unconstrained Movements Involve Different Control Strategies. J. Neurophysiol. 77, 1644–1650 (1997)CrossRefGoogle Scholar
  25. 25.
    Diedrichsen, J., Shadmehr, R., Ivry, R.B.: The coordination of movement: optimal feedback control and byond. Trends Cogn. Sci. 14, 31–39 (2010)CrossRefGoogle Scholar
  26. 26.
    DiMaio, S., Hanuschik, M., Kreaden, U.: The da Vinci surgical system. In: Surgical Robotics, pp 199–217. Springer (2011)Google Scholar
  27. 27.
    DiMaio, S., Hasser, C.: The da Vinci research interface. In: Proceedings of International Conference on Medical Image Computing and Computer Assisted Intervention 2008: Workshop-S5 Systems and Architectures for Computer Assisted Interventions (2008)Google Scholar
  28. 28.
    Dingwell, J.B., Mah, C.D., Mussa-Ivaldi, F.A.: Manipulating objects with internal degrees of freedom: Evidence for model-based control. J. Neurophysiol. 88, 222–235 (2002)CrossRefGoogle Scholar
  29. 29.
    Dingwell, J.B., Smallwood, R.F., Cusumano, J.P.: Trial-to-trial dynamics and learning in a generalized, redundant reaching task. J. Neurophysiol. 109, 225–237 (2013)CrossRefGoogle Scholar
  30. 30.
    Ericsson, K.A.: Deliberate practice and the acquisition and maintenance of expert performance in medicine and related domains. Acad. Med. 79, S70–S81 (2004)CrossRefGoogle Scholar
  31. 31.
    Ernst, M.O., Banks, M.S.: Humans integrate visual and haptic information in a statistically optimal fashion. Nature 415, 429–433 (2002)CrossRefGoogle Scholar
  32. 32.
    Faisal, A.A., Selen, L.P.J., Wolpert, D.M.: Noise in the nervous system. Nat. Rev. Neurosci. 9, 292–303 (2008)CrossRefGoogle Scholar
  33. 33.
    Fergus, P., Haggerty, J., Taylor, M., Bracegirdle, L.: Towards a whole body sensing platform for healthcare applications. In: Whole Body Interaction, pp 135–149. Springer (2011)Google Scholar
  34. 34.
    Fernandes, H.L., Albert, M.V., Kording, K.P.: Measuring generalization of visuomotor perturbations in wrist movements using mobile phones. PLoS ONE 6, e20290 (2011)CrossRefGoogle Scholar
  35. 35.
    Finnegan, K.T., Meraney, A.M., Staff, I., Shichman, S.J.: da Vinci skills simulator construct validation study: correlation of prior robotic experience with overall score and time score simulator performance. Urology 80, 330–336 (2012)CrossRefGoogle Scholar
  36. 36.
    Fitts, P.M.: The information capacity of the human motor system in controlling the amplitude of movement. J. Exp. Psychol. 47, 381–391 (1954)CrossRefGoogle Scholar
  37. 37.
    Flanagan, J.R., Beltzner, M.A.: Independence of perceptual and sensorimotor predictions in the size-weight illusion. Nat. Neurosci. 3, 737–741 (2000)CrossRefGoogle Scholar
  38. 38.
    Flash, T., Hogan, N.: The coordination of arm movements: An experimentally confirmed mathematical model. J. Neurosci. 5, 1688–1703 (1985)CrossRefGoogle Scholar
  39. 39.
    Flash, T., Meirovitch, Y., Barliya, A.: Models of human movement: trajectory planning and inverse kinematics studies. Robot. Auton. Syst. 61, 330–339 (2013)CrossRefGoogle Scholar
  40. 40.
    Franklin, D.W., Wolpert, D.M.: Computational mechanisms of sensorimotor control. Neuron 72, 425–442 (2011)CrossRefGoogle Scholar
  41. 41.
    Fuji, K., Salerno, A., Kumuthan, S., Kwok, K.W., Shetty, K., Yang, G.Z.: Gaze contingent cartesian control of a robotic arm for laparoscopic surgery. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (2013)Google Scholar
  42. 42.
    Gallagher, A., McClure, N., McGuigan, J., Ritchie, K., Sheehy, N.: An ergonomic analysis of the fulcrum effect in the acquisition of endoscopic skills. Endoscopy 30, 617–620 (1998)CrossRefGoogle Scholar
  43. 43.
    Gallagher, A.G., Satava, R.M., Shorten, G.D.: Measuring surgical skill: a rapidly evolving scientific methodology. Surg. Endosc. 1–5 (2013)Google Scholar
  44. 44.
    Ganel, T., Goodale, M.A.: Visual control of action but not perception requires analytical processing of object shape. Nature 426, 664–667 (2003)CrossRefGoogle Scholar
  45. 45.
    Gibo, T.L., Bastian, A.J., Okamura, A.M.: Grip force control during virtual object interaction: effect of force feedback, accuracy demands, and training. Submitted (2013)Google Scholar
  46. 46.
    Gomi, H., Osu, R.: Task-dependent viscoelasticity of human multijoint arm and its spatial characteristics for interaction with environments. J. Neurosci. 18, 8965–8978 (1998)CrossRefGoogle Scholar
  47. 47.
    Goodale, M.A., Milner, A.D.: Separate visual pathways for perception and action. Trends Neurosci. 15, 20–25 (1992)CrossRefGoogle Scholar
  48. 48.
    Guthart, G.S., Salisbury, Jr. J.K.: The IntuitiveTM telesurgery system: overview and application. In: 2000 Proceedings ICRA’00 IEEE International Conference on Robotics and Automation, vol. 1, pp. 618–621. IEEE (2000)Google Scholar
  49. 49.
    Hung, A.J., Jayaratna, I.S., Teruya, K., Desai, M.M., Gill, I.S., Goh, A.C.: Comparative assessment of three standardized robotic surgery training methods. BJU Int. (2013)Google Scholar
  50. 50.
    Hung, A.J., Zehnder, P., Patil, M.B., Cai, J., Ng, C.K., Aron, M., Gill, I.S., Desai, M.M.: Face, content and construct validity of a novel robotic surgery simulator. J. Urol. 186, 1019–1025 (2011)CrossRefGoogle Scholar
  51. 51.
    Intuitive Surgical, I.: (2013)Google Scholar
  52. 52.
    Johansson, R.S., Flanagan, J.R.: Coding and use of tactile signals from the fingertips in object manipulation tasks. Nat. Rev. Neurosci. 10, 345–359 (2009)Google Scholar
  53. 53.
    Jones, D.B., Brewer, J.D., Soper, N.J.: The influence of three-dimensional video systems on laparoscopic task performance. Surg. Laparosc. Endosc. Percutaneous Tech. 6, 191–197 (1996)CrossRefGoogle Scholar
  54. 54.
    Judkins, T.N., Oleynikov, D., Stergiou, N.: Objective evaluation of expert and novice performance during robotic surgical training tasks. Surg. Endosc. 23, 590–597 (2009)CrossRefGoogle Scholar
  55. 55.
    Kandel, E.R., Schwartz, J.H., Jessel, T.M.: Principles of Neural Science. McGraw-Hill, New York (2000)Google Scholar
  56. 56.
    Karniel, A.: Open questions in computational motor control. J. Integr. Neurosci. 10, 385–411 (2011)CrossRefGoogle Scholar
  57. 57.
    Kenney, P.A., Wszolek, M.F., Gould, J.J., Libertino, J.A., Moinzadeh, A.: Face, content, and construct validity of dV-trainer, a novel virtual reality simulator for robotic surgery. Urology 73, 1288–1292 (2009)CrossRefGoogle Scholar
  58. 58.
    Kluzik, J., Diedrichsen, J., Shadmehr, R., Bastian, A.J.: Reach adaptation: what determines whether we learn an internal model of the tool or adapt the model of our arm? J. Neurophysiol. 100, 1455–1464 (2008)CrossRefGoogle Scholar
  59. 59.
    Kording, K.: Decision theory: what “should” the nervous system do? Science 318, 606–610 (2007)CrossRefGoogle Scholar
  60. 60.
    Kording, K.P., Wolpert, D.M.: Bayesian integration in sensorimotor learning. Nature 427, 244–247 (2004)CrossRefGoogle Scholar
  61. 61.
    Kording, K.P., Wolpert, D.M.: Bayesian decision theory in sensorimotor control. Trends Cogn. Sci. 10, 319–326 (2006)CrossRefGoogle Scholar
  62. 62.
    Kowalczyk, K.J., Levy, J.M., Caplan, C.F., Lipsitz, S.R., Yu H-y, GuX, Hu, J.C.: Temporal national trends of minimally invasive and retropubic radical prostatectomy outcomes from 2003 to 2007: results from the 100% medicare sample. Eur. Urol. 61, 803–809 (2012)CrossRefGoogle Scholar
  63. 63.
    Krakauer, J.W., Mazzoni, P.: Human sensorimotor learning: adaptation, skill, and beyond. Curr. Opin. Neurobiol. 21, 636–644 (2011)CrossRefGoogle Scholar
  64. 64.
    Krakauer, J.W., Pine, Z.M., Ghilardi, M.-F., Ghez, C.: Learning of visuomotor transformations for vectorial planning of reaching trajectories. J. Neurosci. 20, 8916–8924 (2000)CrossRefGoogle Scholar
  65. 65.
    Krebs, H.I., Dipietro, L., Levy-Tzedek, S., Fasoli, S., Rykman-Berland, A., Zipse, J., Fawcett, J., Stein, J., Poizner, H., Lo, A., Volpe, B., Hogan, N.: A paradigm shift for rehabilitation robotics. Eng. Med. Biol. Mag. IEEE 27, 61–70 (2008)CrossRefGoogle Scholar
  66. 66.
    Kuschel, M., Di Luca, M., Buss, M., Klatzky, R.L.: Combination and Integration in the perception of visual-haptic compliance information. IEEE Trans. Haptics 3, 234–244 (2010)CrossRefGoogle Scholar
  67. 67.
    Latash, M.L., Scholz, J.P., Schöner, G.: Toward a new theory of motor synergies. Mot. Control 11, 276–308 (2007)CrossRefGoogle Scholar
  68. 68.
    Leib, R., Karniel, A.: Minimum acceleration with constraints of center of mass: a unified model for arm movements and object manipulation. J. Neurophysiol. 108, 1646–1655 (2012)CrossRefGoogle Scholar
  69. 69.
    Lin, H., Shafran, I., Yuh, D., Hager, G.: Towards automatic skill evaluation: detection and segmentation of robot-assisted surgical motions. Comput. Aided Surg. 11, 220–230 (2006)CrossRefGoogle Scholar
  70. 70.
    Liss, M.A., McDougall, E.M.: Robot. Surg. Simulat. Cancer J. 19, 124–129 (2013)Google Scholar
  71. 71.
    Lohse, K.R., Jones, M., Healy, A.F., Sherwood, D.E.: The Role of Attention in Motor Control (2013)Google Scholar
  72. 72.
    Lum, M.J.H., Friedman, D.C.W., Sankaranarayanan, G., King, H., Fodero, K., Leuschke, R., Hannaford, B., Rosen, J., Sinanan, M.N.: The RAVEN: design and validation of a telesurgery system. Int. J. Robot. Res. 28, 1183–1197 (2009)CrossRefGoogle Scholar
  73. 73.
    Lyons, C., Goldfarb, D., Jones, S.L., Badhiwala, N., Miles, B., Link, R., Dunkin, B.J.: Which skills really matter? Proving face, content, and construct validity for a commercial robotic simulator. Surg. Endosc. 1–11 (2013)Google Scholar
  74. 74.
    Maoz, U., Portugaly, E., Flash, T., Weiss, Y.: Noise and the two-thirds power law. Advanc. Neural Informat. Process. Syst. 851–858 (2005)Google Scholar
  75. 75.
    Markram, H.: The blue brain project. Nat. Rev. Neurosci. 7, 153–160 (2006)CrossRefGoogle Scholar
  76. 76.
    Martin, T.A., Keating, J.G., Goodkin, H.P., Bastian, A.J., Thach, W.T.: Throwing while looking through prisms. Brain 119, 1183–1198 (1996)CrossRefGoogle Scholar
  77. 77.
    Mawase, F., Karniel, A.: Evidence for predictive control in lifting series of virtual objects. Exp. Brain Res. 203, 447–452 (2010)CrossRefGoogle Scholar
  78. 78.
    McDougall, E.M.: Validation of surgical simulators. J. Endourol. 21, 244–247 (2007)CrossRefGoogle Scholar
  79. 79.
    McMahan, W., Gewirtz, J., Standish, D., Martin, P., Kunkel, J.A., Lilavois, M., Wedmid, A., Lee, D.I., Kuchenbecker, K.J.: Tool contact acceleration feedback for telerobotic surgery. IEEE Trans. Haptics 4, 210–220 (2011)CrossRefGoogle Scholar
  80. 80.
    Megali, G., Sinigaglia, S., Tonet, O., Dario, P.: Modelling and evaluation of surgical performance using hidden Markov models. IEEE Trans. Biomed. Eng. 53, 1911–1919 (2006)CrossRefGoogle Scholar
  81. 81.
    Morasso, P.: Spatial control of arm movements. Exp. Brain Res. 42, 223–227 (1981)CrossRefGoogle Scholar
  82. 82.
    Müller, H., Sternad, D.: Decomposition of variability in the execution of goal-oriented tasks: three components of skill improvement. J. Exp. Psychol. Hum. Percept. Perform. 30, 212–233 (2004)CrossRefGoogle Scholar
  83. 83.
    Mussa-Ivaldi, F.A., Hogan, N., Bizzi, E.: Neural, mechanical, and geometric factors subserving arm posture in humans. J. Neurosci. 5, 2732–2743 (1985)CrossRefGoogle Scholar
  84. 84.
    Mylonas, G.P., Darzi, A., Zhong Yang, G.: Gaze-contingent control for minimally invasive robotic surgery. Comput. Aided Surg. 11, 256–266 (2006)CrossRefGoogle Scholar
  85. 85.
    Mylonas, G.P., Kwok, K.-W., Darzi, A., Yang, G.-Z.: Gaze-contingent motor channelling and haptic constraints for minimally invasive robotic surgery. In: Medical Image Computing and Computer-Assisted Intervention–MICCAI 2008, pp. 676–683. Springer (2008)Google Scholar
  86. 86.
    Mylonas, G.P., Stoyanov, D., Deligianni, F., Darzi, A., Yang, G.-Z.: Gaze-contingent soft tissue deformation tracking for minimally invasive robotic surgery. In: Medical Image Computing and Computer-Assisted Intervention–MICCAI 2005, pp. 843–850. Springer (2005)Google Scholar
  87. 87.
    Narazaki, K., Oleynikov, D., Stergiou, N.: Objective assessment of proficiency with bimanual inanimate tasks in robotic laparoscopy. J. Laparoendosc. Adv. Surg. Tech. 17, 47–52 (2007)CrossRefGoogle Scholar
  88. 88.
    Nisky, I., Hsieh, M.H., Okamura, A.M.: The Effect of a robot-assisted surgical system on the kinematics of user movements. 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 6257–6260. Osaka, Japan (2013)Google Scholar
  89. 89.
    Nisky, I., Hsieh, M.H., Okamura, A.M.: A framework for analysis of surgeon arm posture variability in robot-assisted surgery. IEEE International Conference on Robotics and Automation, pp. 245–251. Karlsruhe, Germany (2013)Google Scholar
  90. 90.
    Nisky, I., Hsieh, M.H., Okamura, A.M.: Uncontrolled manifold analysis of arm joint angle variability during robotic teleoperation and freehand movement of surgeons and novices. IEEE Trans. Biomed. Eng. 61, 2869–2881 (2014)CrossRefGoogle Scholar
  91. 91.
    Nisky, I., Okamura, A.M., Hsieh, M.H.: Effect of robotic manipulators on movements of novices and surgeons. Surg. Endosc. 28, 2145–2158 (2014)CrossRefGoogle Scholar
  92. 92.
    Nisky, I., Patil, S., Hsieh, M.H., Okamura, A.M.: Kinematic analysis of motor performance in robot-assisted surgery: a preliminary study. In: Medicine Meets Virtual Reality (Studies in Health Technology and Information), vol. 184, pp. 302–308. San Diego (2013)Google Scholar
  93. 93.
    Nisky, I., Pressman, A., Pugh, C.M., Mussa-Ivaldi, F.A., Karniel, A.: Perception and action in teleoperated needle insertion. IEEE Trans. Haptics 4, 155–166 (2011)CrossRefGoogle Scholar
  94. 94.
    Noonan, D.P., Mylonas, G.P., Shang, J., Payne, C.J., Darzi, A., Yang, G.-Z.: Gaze contingent control for an articulated mechatronic laparoscope. In: 2010 3rd IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob), pp. 759–764. IEEE (2010)Google Scholar
  95. 95.
    Okamura, A.M.: Haptic feedback in robot-assisted minimally invasive surgery. Curr. Opin. Urol. 19, 102 (2009)CrossRefGoogle Scholar
  96. 96.
    Pressman, A., Nisky, I., Karniel, A., Mussa-Ivaldi, F.A.: Probing virtual boundaries and the perception of delayed stiffness. Adv. Robot. 22, 119–140 (2008)CrossRefGoogle Scholar
  97. 97.
    Provancher, W.R., Cutkosky, M.R., Kuchenbecker, K.J., Niemeyer, G.: Contact location display for haptic perception of curvature and object motion. Int. J. Robot. Res. 24, 691–702 (2005)CrossRefGoogle Scholar
  98. 98.
    Qadan, M., Curet, M.J., Wren, S.M.: The evolving application of single‐port robotic surgery in general surgery. J. Hepato-Biliary-Pancreatic Sci. (2013)Google Scholar
  99. 99.
    Quek, Z.F., Schorr, S., Nisky, I., Okamura, A.M., Provancher, W.: Sensory augmentation of virtual stiffness using finger pad skin stretch. IEEE World Haptics Conference, pp. 467–472. Daejeon, Korea (2013)Google Scholar
  100. 100.
    Reinkensmeyer, D., Wynne, J.H., Harkema, S.J. A robotic tool for studying locomotor adaptation and rehabilitation. In: 2002 24th Annual Conference and the Annual Fall Meeting of the Biomedical Engineering Society EMBS/BMES Conference on Engineering in Medicine and Biology, 2002 Proceedings of the Second Joint, vol. 3, pp. 2353–2354, vol. 2353 (2002)Google Scholar
  101. 101.
    Reis, J., Schambra, H.M., Cohen, L.G., Buch, E.R., Fritsch, B., Zarahn, E., Celnik, P.A., Krakauer, J.W.: Noninvasive cortical stimulation enhances motor skill acquisition over multiple days through an effect on consolidation. Proc. Natl. Acad. Sci. 106, 1590–1595 (2009)CrossRefGoogle Scholar
  102. 102.
    Reyes, J.M., Smaldone, M.C., Uzzo, R.G., Viterbo, R.: Current status of robot-assisted partial nephrectomy. Curr. Urol. Rep. 13, 24–37 (2012)CrossRefGoogle Scholar
  103. 103.
    Robotic Training Network (2013)Google Scholar
  104. 104.
    Rosen, J., Brown, J.D., Chang, L., Sinanan, M.N., Hannaford, B.: Generalized approach for modeling minimally invasive surgery as a stochastic process using a discrete Markov model. IEEE Trans. Biomed. Eng. 53, 399–413 (2006)CrossRefGoogle Scholar
  105. 105.
    Satava, R., Smith, R., Patel, V.: Fundamentals of Robotic Surgery: Outcomes Measures and Curriculum Development. In: SLS Boston, MA (2012)Google Scholar
  106. 106.
    Scheidt, R.A., Ghez, C.: Separate adaptive mechanisms for controlling trajectory and final position in reaching. J. Neurophysiol. 98, 3600–3613 (2007)CrossRefGoogle Scholar
  107. 107.
    Scholz, J.P., Schoner, G.: The uncontrolled manifold concept: identifying control variables for a functional task. Exp. Brain Res. 126, 289–306 (1999)CrossRefGoogle Scholar
  108. 108.
    Schorr, S., Quek, Z.F., Romano, R., Nisky, I., Provancher, W., Okamura, A.M.: Sensory substitution via cutaneous skin stretch feedback. IEEE International Conference on Robotics and Automation, pp. 2333–2338. Karlsruhe, Germany (2013)Google Scholar
  109. 109.
    Scott, S.H.: Optimal feedback control and the neural basis of volitional motor control. Nat. Rev. Neurosci. 5, 532–546 (2004)CrossRefGoogle Scholar
  110. 110.
    Shadmehr, R.: Computational approaches to motor control. In: R. S.L., (ed.) Encyclopedia of Neuroscience, vol. 3, pp 9–17. Oxford: Academic PressGoogle Scholar
  111. 111.
    Shadmehr, R., Mussa-Ivaldi, F.A.: Adaptive representation of dynamics during learning of a motor task. J. Neurosci. 14, 3208–3224 (1994)CrossRefGoogle Scholar
  112. 112.
    Shadmehr, R., Mussa-Ivaldi, S.: Biological Learning and Control: How the Brain Builds Representations, Predicts Events, and Makes Decisions. MIT Press (2012)Google Scholar
  113. 113.
    Shadmehr, R., Smith, M.A., Krakauer, J.W.: Error correction, sensory prediction, and adaptation in motor control. Annu. Rev. Neurosci. 33, 89–108 (2010)CrossRefGoogle Scholar
  114. 114.
    Shadmehr, R., Wise, S.P.: The Computational Neurobiology of Reaching and Pointing: A Foundation for Motor Learning. MIT Press (2005)Google Scholar
  115. 115.
    Shmuelof, L., Krakauer, J.W., Mazzoni, P.: How is a motor skill learned? Change and invariance at the levels of task success and trajectory control. J. Neurophysiol. 108, 578–594 (2012)CrossRefGoogle Scholar
  116. 116.
    Smith, R., Patel, V., Chauhan, S., Satava, R.: Fundamentals of robotic surgery: outcomes measures and curriculum development. In: NextMed/MMVR 20 San Diego, CA (2013)Google Scholar
  117. 117.
    Svinin, M., Goncharenko, I., Zhi-Wei, L., Hosoe, S.: Reaching movements in dynamic environments: how do we move flexible objects? IEEE Trans. Rob. 22, 724–739 (2006)CrossRefGoogle Scholar
  118. 118.
    Takahashi, C.D., Scheidt, R.A., Reinkensmeyer, D.J.: Impedance control and internal model formation when reaching in a randomly varying dynamical environment. J. Neurophysiol. 86, 1047–1051 (2001)CrossRefGoogle Scholar
  119. 119.
    Tausch, T.J., Kowalewski, T.M., White, L.W., McDonough, P.S., Brand, T.C., Lendvay, T.S.: Content and construct validation of a robotic surgery curriculum using an electromagnetic instrument tracker. J. Urol. 188, 919–923 (2012)CrossRefGoogle Scholar
  120. 120.
    Tewari, A., Sooriakumaran, P., Bloch, D.A., Seshadri-Kreaden, U., Hebert, A.E., Wiklund, P.: Positive surgical margin and perioperative complication rates of primary surgical treatments for prostate cancer: a systematic review and meta-analysis comparing retropubic, laparoscopic, and robotic prostatectomy. Eur. Urol. 62, 1–15 (2012)CrossRefGoogle Scholar
  121. 121.
    Thoroughman, K.A., Shadmehr, R.: Learning of action through adaptive combination of motor primitives. Nature 407, 742–747 (2000)CrossRefGoogle Scholar
  122. 122.
    Todorov, E., Jordan, M.I.: Optimal feedback control as a theory of motor coordination. Nat. Neurosci. 5, 1226–1235 (2002)CrossRefGoogle Scholar
  123. 123.
    Touwen, B.C.L.: How normal is variable, or how variable is normal? Early Human Dev. 34, 1–12 (1993)CrossRefGoogle Scholar
  124. 124.
    Trinh, Q.-D., Sammon, J., Sun, M., Ravi, P., Ghani, K.R., Bianchi, M., Jeong, W., Shariat, S.F., Hansen, J., Schmitges, J.: Perioperative outcomes of robot-assisted radical prostatectomy compared with open radical prostatectomy: results from the nationwide inpatient sample. Eur. Urol. 61, 679–685 (2012)CrossRefGoogle Scholar
  125. 125.
    Tsuji, T., Morasso, P., Goto, K., Ito, K.: Human hand impedance characteristics during maintained posture. Biol. Cybern. 72, 475–485 (1995)zbMATHCrossRefGoogle Scholar
  126. 126.
    Uno, Y., Kawato, M., Suzuki, R.: Formation and control of optimal trajectory in human multijoint arm movement—minimum torque-change model. Biol. Cybern. 61, 89–101 (1989)CrossRefGoogle Scholar
  127. 127.
    Vickers, J.N.: Perception, cognition and decision training: the quiet eye in action. Human Kinetics (2007)Google Scholar
  128. 128.
    Wilson, M., McGrath, J., Vine, S., Brewer, J., Defriend, D., Masters, R.: Psychomotor control in a virtual laparoscopic surgery training environment: gaze control parameters differentiate novices from experts. Surg. Endosc. 24, 2458–2464 (2010)CrossRefGoogle Scholar
  129. 129.
    Wilson, M.R., Vine, S.J., Bright, E., Masters, R.S., Defriend, D., McGrath, J.S.: Gaze training enhances laparoscopic technical skill acquisition and multi-tasking performance: a randomized, controlled study. Surg. Endosc. 25, 3731–3739 (2011)CrossRefGoogle Scholar
  130. 130.
    Wolpert, D.M., Diedrichsen, J., Flanagan, J.R.: Principles of sensorimotor learning. Nat. Rev. Neurosci. 12, 739–751 (2011)CrossRefGoogle Scholar
  131. 131.
    Wolpert, D.M., Ghahramani, Z.: Computational principles of movement neuroscience. Nat. Neurosci. 3, 1212–1217 (2000)CrossRefGoogle Scholar
  132. 132.
    Woodworth, R.S.: Accuracy of voluntary movement. Psychol. Rev. Monogr. Suppl. 3, i–114 (1899)CrossRefGoogle Scholar
  133. 133.
    Yang, G.-Z., Mylonas, G.P., Kwok, K.-W., Chung, A.: Perceptual docking for robotic control. Med. Imag. Augment. Real. 21–30. Springer (2008)Google Scholar
  134. 134.
    Yang, J.-F., Scholz, J., Latash, M.: The role of kinematic redundancy in adaptation of reaching. Exp. Brain Res. 176, 54–69 (2007)CrossRefGoogle Scholar
  135. 135.
    Yarrow, K., Brown, P., Krakauer, J.W.: Inside the brain of an elite athlete: the neural processes that support high achievement in sports. Nat. Rev. Neurosci. 10, 585–597 (2009)CrossRefGoogle Scholar
  136. 136.
    Zago, M., McIntyre, J., Senot, P., Lacquaniti, F.: Visuo-motor coordination and internal models for object interception. Exp. Brain Res. 192, 571–604 (2009)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Medical Research, Intuitive Surgical, Inc.SunnyvaleUSA
  2. 2.Department of Biomedical EngineeringBen-Gurion University of the NegevBeer-ShevaIsrael

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