Advertisement

Autonomous Robots

, Volume 42, Issue 5, pp 957–975 | Cite as

Progress and prospects of the human–robot collaboration

  • Arash Ajoudani
  • Andrea Maria Zanchettin
  • Serena Ivaldi
  • Alin Albu-Schäffer
  • Kazuhiro Kosuge
  • Oussama Khatib
Article
Part of the following topical collections:
  1. Special Issue: Learning for Human-Robot Collaboration

Abstract

Recent technological advances in hardware design of the robotic platforms enabled the implementation of various control modalities for improved interactions with humans and unstructured environments. An important application area for the integration of robots with such advanced interaction capabilities is human–robot collaboration. This aspect represents high socio-economic impacts and maintains the sense of purpose of the involved people, as the robots do not completely replace the humans from the work process. The research community’s recent surge of interest in this area has been devoted to the implementation of various methodologies to achieve intuitive and seamless human–robot-environment interactions by incorporating the collaborative partners’ superior capabilities, e.g. human’s cognitive and robot’s physical power generation capacity. In fact, the main purpose of this paper is to review the state-of-the-art on intermediate human–robot interfaces (bi-directional), robot control modalities, system stability, benchmarking and relevant use cases, and to extend views on the required future developments in the realm of human–robot collaboration.

Keywords

Physical human robot collaboration Progress and prospects Human–robot interaction Human-in-the-loop Human–robot interfaces 

Notes

Acknowledgements

The authors would like to thank and remember Fabrizio Flacco for his spirit, contributions, and enthusiasm for writing this review paper. We will keep his memories alive in our hearts. This work is supported in part by the EU FP7-ICT projects WALKMAN (No. 611832) and CoDyCo (No. 600716); in part by the H2020 Projects SoftPro (No. 688857) and AnDy (No.731540).

References

  1. Adams, J., Bajcsy, R., Košecká, J., Kumar, V., Mintz, M., Mandelbaum, R., et al. (1996). Cooperative material handling by human and robotic agents: Module development and system synthesis. Expert Systems with Applications, 11(2), 89–97.CrossRefGoogle Scholar
  2. Agravante, D., Cherubini, A., Bussy, A., Gergondet, P., & Kheddar, A. (2014a). Collaborative human-humanoid carrying using vision and haptic sensing. In 2014 IEEE International Conference on Robotics and Automation (ICRA) (pp 607–612).Google Scholar
  3. Agravante, D. J., Cherubini, A., Bussy, A., Gergondet, P., & Kheddar, A. (2014b). Collaborative human-humanoid carrying using vision and haptic sensing. In 2014 IEEE international conference on robotics and automation (ICRA) (pp. 607–612). IEEE.Google Scholar
  4. Ajoudani, A. (2016). Transferring human impedance regulation skills to robots. Berlin: Springer.CrossRefGoogle Scholar
  5. Ajoudani, A., Godfrey, S. B., Bianchi, M., Catalano, M. G., 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.CrossRefGoogle Scholar
  6. Al-Jarrah, O., & Zheng, Y. (1997). Arm-manipulator coordination for load sharing using reflexive motion control. In 1997 IEEE international conference on robotics and automation (ICRA) (Vol. 3, pp. 2326–2331).Google Scholar
  7. Al-Jarrah, O. M. & Zheng, Y. F. (1997b). Arm-manipulator coordination for load sharing using reflexive motion control. In Robotics and automation, 1997. Proceedings. 1997 IEEE international conference on, (Vol. 3, pp. 2326–2331). IEEE.Google Scholar
  8. Alami, R., Albu-Schäffer, A., Bicchi, A., Bischoff, R., Chatila, R., De Luca, A., De Santis, A., Giralt, G., Guiochet, J., Hirzinger, G., et al. (2006). Safe and dependable physical human-robot interaction in anthropic domains: State of the art and challenges. In 2006 IEEE/RSJ international conference on intelligent robots and systems (pp. 1–16). IEEE.Google Scholar
  9. 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.CrossRefGoogle Scholar
  10. Albu-Schäffer, 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.zbMATHCrossRefGoogle Scholar
  11. Amor, H. B., Berger, E., Vogt, D., & Jung, B. (2009). Kinesthetic bootstrapping: Teaching motor skills to humanoid robots through physical interaction. In Annual conference on artificial intelligence (pp. 492–499). Springer.Google Scholar
  12. Argall, B. D., & Billard, A. G. (2010). A survey of tactile human–robot interactions. Robotics and Autonomous Systems, 58(10), 1159–1176.CrossRefGoogle Scholar
  13. Bascetta, L., Ferretti, G., Rocco, P., Ardö, H., Bruyninckx, H., Demeester, E., & Di Lello, E. (2011). Towards safe human–robot interaction in robotic cells: An approach based on visual tracking and intention estimation. In 2011 IEEE/RSJ international conference on intelligent robots and systems (pp. 2971–2978). IEEE.Google Scholar
  14. Bauer, A., Wollherr, D., & Buss, M. (2008). Human–robot collaboration: A survey. International Journal of Humanoid Robotics, 5(01), 47–66.CrossRefGoogle Scholar
  15. Bell, C. J., Shenoy, P., Chalodhorn, R., & Rao, R. P. (2008). Control of a humanoid robot by a noninvasive brain–computer interface in humans. Journal of Neural Engineering, 5(2), 214.CrossRefGoogle Scholar
  16. Berret, B., Ivaldi, S., Nori, F., & Sandini, G. (2011). Stochastic optimal control with variable impedance manipulators in presence of uncertainties and delayed feedback. In Proceedings of the 2011 IEEE/RSJ international conference on intelligent robots and systems (IROS) (pp. 4354–4359).Google Scholar
  17. Bestick, A. M., Burden, S. A., Willits, G., Naikal, N., Sastry, S. S., & Bajcsy, R. (2015). Personalized kinematics for human–robot collaborative manipulation. In 2015 IEEE/RSJ international conference on intelligent robots and systems (IROS) (pp. 1037–1044). IEEE.Google Scholar
  18. Böhme, H.-J., Wilhelm, T., Key, J., Schauer, C., Schröter, C., Groß, H.-M., et al. (2003). An approach to multi-modal human–machine interaction for intelligent service robots. Robotics and Autonomous Systems, 44(1), 83–96.CrossRefGoogle Scholar
  19. Buerger, S. P., & Hogan, N. (2006). Relaxing passivity for human–robot interaction. In 2006 IEEE/RSJ international conference on intelligent robots and systems (pp. 4570–4575).Google Scholar
  20. Buerger, S. P., & Hogan, N. (2007). Complementary stability and loop shaping for improved human–robot interaction. IEEE Transactions on Robotics, 23(2), 232–244.CrossRefGoogle Scholar
  21. Burdet, E., Osu, R., Franklin, D., Yoshioka, T., Milner, T., & Kawato, M. (2000). A method for measuring endpoint stiffness during multi-joint arm movements. Journal of Biomechanics, 33(12), 1705–1709.CrossRefGoogle Scholar
  22. 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, 446–449.CrossRefGoogle Scholar
  23. Calinon, S., D’halluin, F., Sauser, E. L., Caldwell, D. G., & Billard, A. G. (2010). Learning and reproduction of gestures by imitation. IEEE Robotics & Automation Magazine, 17(2), 44–54.CrossRefGoogle Scholar
  24. Carlson, T., & Demiris, Y. (2012). Collaborative control for a robotic wheelchair: Evaluation of performance, attention, and workload. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 42(3), 876–888.CrossRefGoogle Scholar
  25. Castellini, C., Artemiadis, P., Wininger, M., Ajoudani, A., Alimusaj, M., Bicchi, A., et al. (2014). Proceedings of the first workshop on peripheral machine interfaces: Going beyond traditional surface electromyography. Frontiers in Neurorobotics, 8, 22.CrossRefGoogle Scholar
  26. Cherubini, A., Passama, R., Crosnier, A., Lasnier, A., & Fraisse, P. (2016). Collaborative manufacturing with physical human–robot interaction. Robotics and Computer-Integrated Manufacturing, 40, 1–13.CrossRefGoogle Scholar
  27. Cherubini, A., Passama, R., Meline, A., Crosnier, A., & Fraisse, P. (2013). Multimodal control for human-robot cooperation. In 2013 IEEE/RSJ international conference on intelligent robots and systems (pp. 2202–2207). IEEE.Google Scholar
  28. Chuy, O., Hirata, Y., & Kosuge, K. (2006). A new control approach for a robotic walking support system in adapting user characteristics. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 36(6), 725–733.CrossRefGoogle Scholar
  29. Colgate, E., & Hogan, N. (1989). An analysis of contact instability in terms of passive physical equivalents. In Proceedings, 1989 international conference on robotics and automation (pp. 404–409).Google Scholar
  30. Colgate, J. E., & Hogan, N. (1988). Robust control of dynamically interacting systems. International Journal of Control, 48(1), 65–88.MathSciNetzbMATHCrossRefGoogle Scholar
  31. Colomé, A., Planells, A., & Torras, C. (2015). A friction-model-based framework for reinforcement learning of robotic tasks in non-rigid environments. In 2015 IEEE international conference on robotics and automation (ICRA) (pp. 5649–5654). IEEE.Google Scholar
  32. Corrales, J. A., Candelas, F., & Torres, F. (2008). Hybrid tracking of human operators using IMU/UWB data fusion by a Kalman filter. In 2008 3rd ACM/IEEE international conference on human–robot interaction (HRI) (pp. 193–200). IEEE.Google Scholar
  33. De Santis, A., Lippiello, V., Siciliano, B., & Villani, L. (2007). Human–robot interaction control using force and vision. In C. Bonivento, L. Marconi, C. Rossi, & A. Isidori (Eds.), Advances in control theory and applications (pp. 51–70). Berlin: Springer.Google Scholar
  34. De Santis, A., Siciliano, B., De Luca, A., & Bicchi, A. (2008). An atlas of physical human–robot interaction. Mechanism and Machine Theory, 43(3), 253–270.zbMATHCrossRefGoogle Scholar
  35. De Schutter, J., De Laet, T., Rutgeerts, J., Decré, W., Smits, R., Aertbeliën, E., et al. (2007). Constraint-based task specification and estimation for sensor-based robot systems in the presence of geometric uncertainty. The International Journal of Robotics Research, 26(5), 433–455.CrossRefGoogle Scholar
  36. de Vlugt, E., Schouten, A. C., van der Helm, F. C., Teerhuis, P. C., & Brouwn, G. G. (2003). A force-controlled planar haptic device for movement control analysis of the human arm. Journal of Neuroscience Methods, 129(2), 151–168.CrossRefGoogle Scholar
  37. Dimeas, F., & Aspragathos, N. (2016). Online stability in human–robot cooperation with admittance control. IEEE Transactions on Haptics, 9(2), 267–278.CrossRefGoogle Scholar
  38. Donner, P., & Buss, M. (2016a). Cooperative swinging of complex pendulum-like objects: Experimental evaluation. IEEE Transactions on Robotics, 32(3), 744–753.CrossRefGoogle Scholar
  39. Donner, P., & Buss, M. (2016b). Cooperative swinging of complex pendulum-like objects: Experimental evaluation. IEEE Transactions on Robotics, 32(3), 744–753.CrossRefGoogle Scholar
  40. Dragan, A., Lee, K., & Srinivasa, S. (2013). Legibility and predictability of robot motion. In Human–robot interaction. Pittsburgh, PA.Google Scholar
  41. Dragan, A., & Srinivasa, S. (2012). Formalizing assistive teleoperation. In Robotics: Science and systems. Pittsburgh, PA.Google Scholar
  42. Duchaine, V., & Gosselin, C. (2007). General model of human–robot cooperation using a novel velocity based variable impedance control. In Eurohaptics conference and symposium on haptic interfaces for virtual environment and teleoperator systems, 2rd joint (pp. 446–451).Google Scholar
  43. Duchaine, V., & Gosselin, C. (2009). Safe, stable and intuitive control for physical human–robot interaction. In IEEE international conference on robotics and automation, 2009. ICRA’09 (pp. 3383–3388). IEEE.Google Scholar
  44. Duchaine, V. & Gosselin, C. M. (2008). Investigation of human–robot interaction stability using Lyapunov theory. In IEEE international conference on robotics and automation, 2008. ICRA 2008. (pp. 2189–2194). IEEE.Google Scholar
  45. Dumora, J., Geffard, F., Bidard, C., Brouillet, T., & Fraisse, P. (2012). Experimental study on haptic communication of a human in a shared human–robot collaborative task. In IEEE/RSJ international conference on intelligent robots and systems (IROS) (pp. 5137–5144).Google Scholar
  46. Ebert, D. M., & Henrich, D. D. (2002). Safe human–robot-cooperation: Image-based collision detection for industrial robots. In IEEE/RSJ international conference on intelligent robots and systems, 2002 (Vol. 2, pp. 1826–1831). IEEE.Google Scholar
  47. Edsinger, A., & Kemp, C. C. (2007). Human–robot interaction for cooperative manipulation: Handing objects to one another. In RO-MAN 2007—the 16th IEEE international symposium on robot and human interactive communication (pp. 1167–1172). IEEE.Google Scholar
  48. Eppinger, S., & Seering, W. (1987). Understanding bandwidth limitations in robot force control. In Proceedings. 1987 IEEE international conference on robotics and automation (Vol. 4, pp. 904–909).Google Scholar
  49. Erden, M. S., & Billard, A. (2014). End-point impedance measurements at human hand during interactive manual welding with robot. In 2014 IEEE international conference on robotics and automation (ICRA) (pp. 126–133). IEEE.Google Scholar
  50. 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).Google Scholar
  51. Evrard, P. & Kheddar, A. (2009). Homotopy switching model for dyad haptic interaction in physical collaborative tasks. In Eurohaptics conference and symposium on haptic interfaces for virtual environment and teleoperator systems, 3rd joint (pp. 45–50).Google Scholar
  52. Farina, D., Jiang, N., Rehbaum, H., Holobar, A., Graimann, B., Dietl, H., et al. (2014). The extraction of neural information from the surface EMG for the control of upper-limb prostheses: Emerging avenues and challenges. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 22(4), 797–809.CrossRefGoogle Scholar
  53. Farry, K., Walker, I., & Baraniuk, R. (1996). Myoelectric teleoperation of a complex robotic hand. IEEE Transactions on Robotics and Automation, 12(5), 775–788.CrossRefGoogle Scholar
  54. Feil-Seifer, D., Skinner, K., & Matarić, M. J. (2007). Benchmarks for evaluating socially assistive robotics. Interaction Studies, 8(3), 423–439.CrossRefGoogle Scholar
  55. Fernandez, V., Balaguer, C., Blanco, D., & Salichs, M. A. (2001). Active human–mobile manipulator cooperation through intention recognition. In IEEE international conference on robotics and automation, 2001. Proceedings 2001 ICRA, volume 3, pages 2668–2673. IEEE.Google Scholar
  56. Ficuciello, F., Romano, A., Villani, L., & Siciliano, B. (2014). Cartesian impedance control of redundant manipulators for human–robot co-manipulation. In 2014 IEEE/RSJ international conference on intelligent robots and systems (pp. 2120–2125). IEEE.Google Scholar
  57. Fleischer, C., & Hommel, G. (2008). A human–exoskeleton interface utilizing electromyography. IEEE Transactions on Robotics, 24(4), 872–882.CrossRefGoogle Scholar
  58. Fong, T., Nourbakhsh, I., & Dautenhahn, K. (2003). A survey of socially interactive robots. Robotics and Autonomous Systems, 42(3), 143–166.zbMATHCrossRefGoogle Scholar
  59. Franklin, D. W., Burdet, E., Tee, K. P., Osu, R., Chew, C.-M., Milner, T. E., et al. (2008). Cns learns stable, accurate, and efficient movements using a simple algorithm. The Journal of Neuroscience, 28(44), 11165–11173.CrossRefGoogle Scholar
  60. Franklin, D. W., Liaw, G., Milner, T. E., Osu, R., Burdet, E., & Kawato, M. (2007). Endpoint stiffness of the arm is directionally tuned to instability in the environment. The Journal of Neuroscience, 27(29), 7705–7716.CrossRefGoogle Scholar
  61. Franklin, D. W., Osu, R., Burdet, E., Kawato, M., & Milner, T. E. (2003). Adaptation to stable and unstable dynamics achieved by combined impedance control and inverse dynamics model. Journal of Neurophysiology, 90(5), 3270–3282.CrossRefGoogle Scholar
  62. Freedy, A., DeVisser, E., Weltman, G., & Coeyman, N. (2007). Measurement of trust in human–robot collaboration. In International symposium on collaborative technologies and systems, 2007. CTS 2007 (pp. 106–114). IEEE.Google Scholar
  63. Fumagalli, M., Ivaldi, S., Randazzo, M., Natale, L., Metta, G., Sandini, G., et al. (2012). Force feedback exploiting tactile and proximal force/torque sensing. Autonomous Robots, 33(4), 381–398.CrossRefGoogle Scholar
  64. Gams, A., Nemec, B., Ijspeert, A., & Ude, A. (2014). Coupling movement primitives: Interaction with the environment and bimanual tasks. IEEE Transactions on Robotics, 30(4), 816–830.CrossRefGoogle Scholar
  65. Gijsberts, A., Bohra, R., Sierra González, D., Werner, A., Nowak, M., Caputo, B., et al. (2014). Stable myoelectric control of a hand prosthesis using non-linear incremental learning. Frontiers in Neurorobotics, 8, 8.CrossRefGoogle Scholar
  66. Glassmire, J., O’Malley, M., Bluethmann, W., & Ambrose, R. (2004). Cooperative manipulation between humans and teleoperated agents. In 12th international symposium on haptic interfaces for virtual environment and teleoperator systems, 2004. HAPTICS’04. Proceedings (pp. 114–120). IEEE.Google Scholar
  67. Godfrey, S., Ajoudani, A., Catalano, M., Grioli, G., & Bicchi, A. (2013). A synergy-driven approach to a myoelectric hand. In 2013 IEEE international conference on rehabilitation robotics (ICORR) (pp. 1–6). IEEE.Google Scholar
  68. Gordon, A. M., Westling, G., Cole, K. J., & Johansson, R. S. (1993). Memory representations underlying motor commands used during manipulation of common and novel objects. Journal of Neurophysiology, 69(6), 1789–1796.CrossRefGoogle Scholar
  69. Green, S. A., Billinghurst, M., Chen, X., & Chase, J. G. (2008). Human-robot collaboration: A literature review and augmented reality approach in design. International Journal of Advanced Robotic Systems, 5(1), 1.CrossRefGoogle Scholar
  70. Green, S. A., Chase, J. G., Chen, X., & Billinghurst, M. (2009). Evaluating the augmented reality human–robot collaboration system. International Journal of Intelligent Systems Technologies and Applications, 8(1–4), 130–143.Google Scholar
  71. Gribovskaya, E., Kheddar, A., & Billard, A. (2011a). Motion learning and adaptive impedance for robot control during physical interaction with humans. In 2011 IEEE international conference on robotics and automation (ICRA) (pp. 4326–4332).Google Scholar
  72. Gribovskaya, E., Kheddar, A., & Billard, A. (2011b). Motion learning and adaptive impedance for robot control during physical interaction with humans. In 2011 IEEE international conference on robotics and automation (ICRA) (pp. 4326–4332). IEEE.Google Scholar
  73. Haddadin, S., Albu-Schäffer, A., & Hirzinger, G. (2009). Requirements for safe robots: Measurements, analysis and new insights. The International Journal of Robotics Research, 28(11–12), 1507–1527.CrossRefGoogle Scholar
  74. Hawkins, K. P., Vo, N., Bansal, S., & Bobick, A. F. (2013). Probabilistic human action prediction and wait-sensitive planning for responsive human–robot collaboration. In 2013 13th IEEE-RAS international conference on humanoid robots (humanoids) (pp. 499–506). IEEE.Google Scholar
  75. Hegel, F., Krach, S., Kircher, T., Wrede, B., & Sagerer, G. (2008). Understanding social robots: A user study on anthropomorphism. In RO-MAN 2008—the 17th IEEE international symposium on robot and human interactive communication (pp. 574–579). IEEE.Google Scholar
  76. Hogan, N. (1985). Impedance control: An approach to manipulation: Part II implementation. Journal of Dynamic Systems, Measurement, and Control, 107(1), 8–16.zbMATHCrossRefGoogle Scholar
  77. Horiguchi, Y., Sawaragi, T., & Akashi, G. (2000). Naturalistic human–robot collaboration based upon mixed-initiative interactions in teleoperating environment. In 2000 IEEE international conference on systems, man, and cybernetics (Vol. 2, pp. 876–881). IEEE.Google Scholar
  78. Hwang, J., Park, T., & Hwang, W. (2013). The effects of overall robot shape on the emotions invoked in users and the perceived personalities of robot. Applied Ergonomics, 44(3), 459–471.CrossRefGoogle Scholar
  79. 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.CrossRefGoogle Scholar
  80. Ikeura, R., & Inooka, H. (1995). Variable impedance control of a robot for cooperation with a human. In 1995 IEEE international conference on robotics and automation (ICRA) (Vol. 3, pp. 3097–3102).Google Scholar
  81. Ikeura, R., & Inooka, H. (1995b). Variable impedance control of a robot for cooperation with a human. In 1995 IEEE international conference on robotics and automation, 1995. Proceedings (Vol. 3, pp. 3097–3102). IEEE.Google Scholar
  82. Ivaldi, S., Anzalone, S., Rousseau, W., Sigaud, O., & Chetouani, M. (2014). Robot initiative in a team learning task increases the rhythm of interaction but not the perceived engagement. Frontiers in Neurorobotics, 8, 5.CrossRefGoogle Scholar
  83. Ivaldi, S., Lefort, S., Peters, J., Chetouani, M., Provasi, J., & Zibetti, E. (2016). Towards engagement models that consider individual factors in HRI: On the relation of extroversion and negative attitude towards robots to gaze and speech during a human-robot assembly task. International Journal of Social Robotics, 9, 63–86.CrossRefGoogle Scholar
  84. Ivaldi, S., Sigaud, O., Berret, B., & Nori, F. (2012). From humans to humanoids: The optimal control framework. Paladyn, 3(2), 75–91.Google Scholar
  85. Jarrasse, N., Paik, J., Pasqui, V., & Morel, G. (2008). How can human motion prediction increase transparency? In Proceedings of the IEEE international conference on robotics and automation (ICRA’08) (pp. 2134–2139). Pasadena, California, US.Google Scholar
  86. Jarrasse, N., Sanguinetti, V., & Burdet, E. (2013). Slaves no longer: Review on role assignment for human–robot joint motor action. Adaptive Behavior, 22, 70–82.CrossRefGoogle Scholar
  87. Jiang, N., Englehart, K., & Parker, P. (2009). Extracting simultaneous and proportional neural control information for multiple-DOF prostheses from the surface electromyographic signal. IEEE Transactions on Biomedical Engineering, 56(4), 1070–1080.CrossRefGoogle Scholar
  88. Johansson, R. S. (1998). Sensory input and control of grip. In Sensory guidance of movement (pp. 45–59). London: John Wiley & Sons.Google Scholar
  89. Kahn, P. H., Ishiguro, H., Friedman, B., & Kanda, T. (2006). What is a human?-Toward psychological benchmarks in the field of human–robot interaction. In The 15th IEEE international symposium on robot and human interactive communication, 2006. ROMAN 2006 (pp. 364–371). IEEE.Google Scholar
  90. Kaneko, K., Harada, K., Kanehiro, F., Miyamori, G., & Akachi, K. (2008). Humanoid robot hrp-3. In 2008 IEEE/RSJ international conference on intelligent robots and systems (pp. 2471–2478). IEEE.Google Scholar
  91. Khansari-Zadeh, S., & Billard, A. (2011). Learning stable nonlinear dynamical systems with gaussian mixture models. IEEE Transactions on Robotics, 27(5), 943–957.CrossRefGoogle Scholar
  92. Khansari-Zadeh, S., Kronander, K., & Billard, A. (2014). Modeling robot discrete movements with state-varying stiffness and damping: A framework for integrated motion generation and impedance control. In RSS.Google Scholar
  93. Khatib, O., Demircan, E., De Sapio, V., Sentis, L., Besier, T., & Delp, S. (2009). Robotics-based synthesis of human motion. Journal of Physiology-Paris, 103(3), 211–219.CrossRefGoogle Scholar
  94. Kilner, J. M., Paulignan, Y., & Blakemore, S.-J. (2003). An interference effect of observed biological movement on action. Current Biology, 13(6), 522–525.CrossRefGoogle Scholar
  95. Kim, S., Kim, C., & Park, J. H. (2006). Human-like arm motion generation for humanoid robots using motion capture database. In 2006 IEEE/RSJ international conference on intelligent robots and systems (pp. 3486–3491). IEEE.Google Scholar
  96. Kim, W., Lee, J., Tsagarakis, N., & Ajoudani, A. (2017). A real-time and reduced-complexity approach to the detection and monitoring of static joint overloading in humans. In International conference on rehabilitation robotics (ICORR).Google Scholar
  97. Kimura, H., Horiuchi, T., & Ikeuchi, K. (1999). Task-model based human robot cooperation using vision. In 1999 IEEE/RSJ international conference on intelligent robots and systems, 1999. IROS’99. Proceedings (Vol. 2, pp. 701–706). IEEE.Google Scholar
  98. Klingspor, V., Demiris, J., & Kaiser, M. (1997). Human–robot communication and machine learning. Applied Artificial Intelligence, 11(7), 719–746.Google Scholar
  99. Kosuge, K., Hashimoto, S., & Yoshida, H. (1998). Human-robots collaboration system for flexible object handling. In 1998 IEEE international conference on robotics and automation, 1998. Proceedings (Vol. 2, pp. 1841–1846). IEEE.Google Scholar
  100. Kosuge, K., & Kazamura, N. (1997a). Control of a robot handling an object in cooperation with a human. In 6th IEEE international workshop on robot and human communication (pp. 142–147).Google Scholar
  101. Kosuge, K., & Kazamura, N. (1997b). Control of a robot handling an object in cooperation with a human. In 6th IEEE international workshop on robot and human communication, 1997. RO-MAN’97. Proceedings (pp. 142–147). IEEE.Google Scholar
  102. Kronander, K., & Billard, A. (2014). Learning compliant manipulation through kinesthetic and tactile human–robot interaction. IEEE Transactions on Haptics, 7(3), 367–380.CrossRefGoogle Scholar
  103. Krüger, J., Lien, T. K., & Verl, A. (2009). Cooperation of human and machines in assembly lines. CIRP Annals-Manufacturing Technology, 58(2), 628–646.CrossRefGoogle Scholar
  104. Kruse, D., Radke, R. J., & Wen, J. T. (2015). Collaborative human-robot manipulation of highly deformable materials. In 2015 IEEE international conference on robotics and automation (ICRA) (pp. 3782–3787). IEEE.Google Scholar
  105. Kulic, D., & Croft, E. A. (2007). Affective state estimation for human–robot interaction. IEEE Transactions on Robotics, 23(5), 991–1000.CrossRefGoogle Scholar
  106. Lacevic, B., & Rocco, P. (2011). Closed-form solution to controller design for human–robot interaction. Journal of Dynamic Systems, Measurement, and Control, 133(2), 024501–024501.CrossRefGoogle Scholar
  107. Lackey, S., Barber, D., Reinerman, L., Badler, N. I., & Hudson, I. (2011). Defining next-generation multi-modal communication in human robot interaction. In Proceedings of the human factors and ergonomics society annual meeting (Vol. 55, pp. 461–464). SAGE Publications.Google Scholar
  108. Lallée, S., Pattacini, U., Lemaignan, S., Lenz, A., Melhuish, C., Natale, L., et al. (2012). Towards a platform-independent cooperative human robot interaction system: III an architecture for learning and executing actions and shared plans. IEEE Transactions on Autonomous Mental Development, 4(3), 239–253.CrossRefGoogle Scholar
  109. Lamy, X., Colledani, F., Geffard, F., Measson, Y., & Morel, G. (2009). Achieving efficient and stable comanipulation through adaptation to changes in human arm impedance. In IEEE international conference on robotics and automation (ICRA’09) (pp. 265–271). Kobe, Japan.Google Scholar
  110. Lawitzky, M., Medina, J., Lee, D., & Hirche, S. (2012a). Feedback motion planning and learning from demonstration in physical robotic assistance: Differences and synergies. In 2012 IEEE/RSJ international conference on intelligent robots and systems (IROS) (pp. 3646–3652).Google Scholar
  111. Lawitzky, M., Medina, J. R., Lee, D., & Hirche, S. (2012b). Feedback motion planning and learning from demonstration in physical robotic assistance: Differences and synergies. In 2012 IEEE/RSJ international conference on intelligent robots and systems (pp. 3646–3652). IEEE.Google Scholar
  112. Lecours, A., Mayer-St-Onge, B., & Gosselin, C. (2012). Variable admittance control of a four-degree-of-freedom intelligent assist device. In 2012 IEEE international conference on robotics and automation (ICRA) (pp. 3903–3908). IEEE.Google Scholar
  113. Lee, D., & Ott, C. (2011). Incremental kinesthetic teaching of motion primitives using the motion refinement tube. Autonomous Robots, 31(2–3), 115–131.CrossRefGoogle Scholar
  114. Lee, S. Y., Lee, K. Y., Lee, S. H., Kim, J. W., & Han, C. S. (2007). Human–robot cooperation control for installing heavy construction materials. Autonomous Robots, 22(3), 305–319.CrossRefGoogle Scholar
  115. Levratti, A., De Vuono, A., Fantuzzi, C., & Secchi, C. (2016). Tirebot: A novel tire workshop assistant robot. In 2016 IEEE international conference on advanced intelligent mechatronics (AIM) (pp. 733–738). IEEE.Google Scholar
  116. Li, Z., Hofemann, N., Fritsch, J., and Sagerer, G. (2005). Hierarchical modeling and recognition of manipulative gesture. In Proceedings of the workshop on modeling people and human interaction at the IEEE international conference on computer vision (Vol. 77).Google Scholar
  117. Maeda, Y., Takahashi, A., Hara, T., & Arai, T. (2001). Human-robot cooperation with mechanical interaction based on rhythm entrainment-realization of cooperative rope turning. In IEEE international conference on robotics and automation, 2001. Proceedings 2001 ICRA (Vol. 4, pp. 3477–3482). IEEE.Google Scholar
  118. Magnanimo, V., Saveriano, M., Rossi, S., & Lee, D. (2014). A bayesian approach for task recognition and future human activity prediction. In The 23rd IEEE international symposium on robot and human interactive communication (pp. 726–731). IEEE.Google Scholar
  119. Magrini, E., Flacco, F., & De Luca, A. (2014). Estimation of contact forces using a virtual force sensor. In 2014 IEEE/RSJ international conference on intelligent robots and systems (pp. 2126–2133). IEEE.Google Scholar
  120. Maurice, P., Padois, V., Measson, Y., & Bidaud, P. (2017). Human-oriented design of collaborative robots. International Journal of Industrial Ergonomics, 57, 88–102.CrossRefGoogle Scholar
  121. Medina, J., Shelley, M., Lee, D., Takano, W., & Hirche, S. (2012a). Towards interactive physical robotic assistance: Parameterizing motion primitives through natural language. In RO-MAN, 2012 IEEE (pp. 1097–1102).Google Scholar
  122. Medina, J. R., Shelley, M., Lee, D., Takano, W., & Hirche, S. (2012b). Towards interactive physical robotic assistance: Parameterizing motion primitives through natural language. In 2012 IEEE RO-MAN: The 21st IEEE international symposium on robot and human interactive communication (pp. 1097–1102). IEEE.Google Scholar
  123. Mittendorfer, P., Yoshida, E., & Cheng, G. (2015). Realizing whole-body tactile interactions with a self-organizing, multi-modal artificial skin on a humanoid robot. Advanced Robotics, 29(1), 51–67.CrossRefGoogle Scholar
  124. Miyake, Y., & Shimizu, H. (1994). Mutual entrainment based human–robot communication field-paradigm shift from human interface to communication field. In 3rd IEEE international workshop on robot and human communication, 1994. RO-MAN’94 Nagoya, Proceedings (pp. 118–123). IEEE.Google Scholar
  125. Mojtahedi, K., Whitsell, B., Artemiadis, P., & Santello, M. (2017). Communication and inference of intended movement direction during human–human physical interaction. Frontiers in Neurorobotics, 11, 21.CrossRefGoogle Scholar
  126. Morasso, P., Casadio, M., Sanguineti, V., Squeri, V., & Vergaro, E. (2007). Robot therapy: The importance of haptic interaction. In Virtual rehabilitation 2007 (pp. 70–77). IEEE.Google Scholar
  127. Morel, G., Malis, E., & Boudet, S. (1998). Impedance based combination of visual and force control. In 1998 IEEE international conference on robotics and automation, 1998. Proceedings (Vol. 2, pp. 1743–1748). IEEE.Google Scholar
  128. Mörtl, 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.CrossRefGoogle Scholar
  129. Murphy, R. R. (2004). Human–robot interaction in rescue robotics. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 34(2), 138–153.MathSciNetCrossRefGoogle Scholar
  130. Nagai, Y., & Rohlfing, K. J. (2009). Computational analysis of motionese toward scaffolding robot action learning. IEEE Transactions on Autonomous Mental Development, 1(1), 44–54.CrossRefGoogle Scholar
  131. Napier, J . R. (1956). The prehensile movements of the human hand. Bone & Joint Journal, 38–B(4), 902–913.Google Scholar
  132. Newman, W. S. (1992). Stability and performance limits of interaction controllers. Journal of Dynamic Systems, Measurement, and Control, 114(4), 563–570.zbMATHCrossRefGoogle Scholar
  133. Nohama, P., Lopes, A. V., & Cliquet, A. (1995). Electrotactile stimulator for artificial proprioception. Artificial Organs, 19(3), 225–230.CrossRefGoogle Scholar
  134. Ogata, T., Masago, N., Sugano, S., & Tani, J. (2003). Interactive learning in human-robot collaboration. In 2003 IEEE/RSJ international conference on intelligent robots and systems, 2003. (IROS 2003). Proceedings (Vol. 1, pp. 162–167). IEEE.Google Scholar
  135. Ott, C., Eiberger, O., Friedl, W., Bauml, B., Hillenbrand, U., Borst, C., Albu-Schaffer, A., Brunner, B., Hirschmuller, H., Kielhofer, S., et al. (2006). A humanoid two-arm system for dexterous manipulation. In 2006 6th IEEE-RAS international conference on humanoid robots (pp. 276–283). IEEE.Google Scholar
  136. Oztop, E., Franklin, D. W., Chaminade, T., & Cheng, G. (2005). Human–humanoid interaction: Is a humanoid robot perceived as a human? International Journal of Humanoid Robotics, 2(04), 537–559.CrossRefGoogle Scholar
  137. Palunko, I., Donner, P., Buss, M., & Hirche, S. (2014). Cooperative suspended object manipulation using reinforcement learning and energy-based control. In 2014 IEEE/RSJ international conference on intelligent robots and systems (IROS) (pp. 885–891).Google Scholar
  138. Pecchinenda, A. (1996). The affective significance of skin conductance activity during a difficult problem-solving task. Cognition & Emotion, 10(5), 481–504.CrossRefGoogle Scholar
  139. Perzanowski, D., Schultz, A. C., & Adams, W. (1998). Integrating natural language and gesture in a robotics domain. In Intelligent control (ISIC), 1998. Held jointly with IEEE international symposium on computational intelligence in robotics and automation (CIRA), intelligent systems and semiotics (ISAS), Proceedings (pp. 247–252). IEEE.Google Scholar
  140. Peternel, L., & Babič, J. (2013). Learning of compliant human–robot interaction using full-body haptic interface. Advanced Robotics, 27(13), 1003–1012.CrossRefGoogle Scholar
  141. 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.CrossRefGoogle Scholar
  142. 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.CrossRefGoogle Scholar
  143. Peternel, L., Rozo, L., Caldwell, D., & Ajoudani, A. (2016b). Adaptation of robot physical behaviour to human fatigue in human–robot co-manipulation. In 2016 IEEE-RAS international conference on humanoid robots (humanoids).Google Scholar
  144. Peternel, L., Tsagarakis, N., & Ajoudani, A. (2016c). Towards multi-modal intention interfaces for human–robot co-manipulation. In 2016 IEEE/RSJ international conference on intelligent robots and systems (IROS).Google Scholar
  145. Petit, M., Lallée, S., Boucher, J.-D., Pointeau, G., Cheminade, P., Ognibene, D., et al. (2013). The coordinating role of language in real-time multimodal learning of cooperative tasks. IEEE Transactions on Autonomous Mental Development, 5(1), 3–17.CrossRefGoogle Scholar
  146. Piovesan, D., Morasso, P., Giannoni, P., & Casadio, M. (2013). Arm stiffness during assisted movement after stroke: The influence of visual feedback and training. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 21(3), 454–465.CrossRefGoogle Scholar
  147. Plagemann, C., Ganapathi, V., Koller, D., & Thrun, S. (2010). Real-time identification and localization of body parts from depth images. In 2010 IEEE international conference on robotics and automation (ICRA) (pp. 3108–3113). IEEE.Google Scholar
  148. Radford, N. A., Strawser, P., Hambuchen, K., Mehling, J. S., Verdeyen, W. K., Donnan, A. S., et al. (2015). Valkyrie: Nasa’s first bipedal humanoid robot. Journal of Field Robotics, 32(3), 397–419.CrossRefGoogle Scholar
  149. Ragaglia, M., Zanchettin, A. M., Bascetta, L., & Rocco, P. (2016). Accurate sensorless lead-through programming for lightweight robots in structured environments. Robotics and Computer-Integrated Manufacturing, 39, 9–21.CrossRefGoogle Scholar
  150. Rani, P., Liu, C., Sarkar, N., & Vanman, E. (2006). An empirical study of machine learning techniques for affect recognition in human–robot interaction. Pattern Analysis and Applications, 9(1), 58–69.CrossRefGoogle Scholar
  151. Rani, P., Sarkar, N., Smith, C. A., & Kirby, L. D. (2004). Anxiety detecting robotic system-towards implicit human–robot collaboration. Robotica, 22(01), 85–95.CrossRefGoogle Scholar
  152. Reed, K. B. (2012). Cooperative physical human–human and human–robot interaction. In A. Peer & C. Giachritsis (Eds.), Immersive Multimodal Interactive Presence., Series on Touch and Haptic Systems London: Springer.Google Scholar
  153. Reed, K. B., & Peshkin, M. A. (2008). Physical collaboration of human–human and human–robot teams. IEEE Transactions on Haptics, 1(2), 108–120.CrossRefGoogle Scholar
  154. Rosen, J., Brand, M., Fuchs, M. B., & Arcan, M. (2001). A myosignal-based powered exoskeleton system. IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans, 31(3), 210–222.CrossRefGoogle Scholar
  155. 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 2015 IEEE/RSJ international conference on intelligent robots and systems (IROS) (pp. 1024–1030). IEEE.Google Scholar
  156. Rozo, L., Calinon, S., & Caldwell, D. G. (2014). Learning force and position constraints in human-robot cooperative transportation. In 2014 RO-MAN: The 23rd IEEE international symposium on robot and human interactive communication (pp. 619–624). IEEE.Google Scholar
  157. Rozo, L., Calinon, S., Caldwell, D. G., Jimenez, P., & Torras, C. (2013). Learning collaborative impedance-based robot behaviors. In: AAAI conference on artificial intelligence (pp. 1422–1428). Bellevue, WA, USA.Google Scholar
  158. Rozo, L., Calinon, S., Caldwell, D. G., Jimenez, P., & Torras, C. (2016). Learning physical collaborative robot behaviors from human demonstrations. IEEE Transactions on Robotics, 32(3), 513–527.CrossRefGoogle Scholar
  159. Sakita, K., Ogawara, K., Murakami, S., Kawamura, K., & Ikeuchi, K. (2004). Flexible cooperation between human and robot by interpreting human intention from gaze information. In 2004 IEEE/RSJ international conference on intelligent robots and systems, 2004. (IROS 2004). Proceedings (Vol. 1, pp. 846–851). IEEE.Google Scholar
  160. Sebanz, N., Bekkering, H., & Knoblich, G. (2006). Joint action: Bodies and minds moving together. Trends in Cognitive Sciences, 10(2), 70–76.CrossRefGoogle Scholar
  161. Shadmehr, R., & Mussa-Ivaldi, F. (1994a). Adaptive representation of dynamics during learning of a motor task. Journal of Neuroscience, 14(5), 3208–3224.CrossRefGoogle Scholar
  162. Shadmehr, R., & Mussa-Ivaldi, F. A. (1994b). Adaptive representation of dynamics during learning of a motor task. The Journal of Neuroscience, 14(5), 3208–3224.CrossRefGoogle Scholar
  163. Shannon, G. (1976). A comparison of alternative means of providing sensory feedback on upper limb prostheses. Medical and Biological Engineering, 14(3), 289–294.CrossRefGoogle Scholar
  164. Squeri, V., Masia, L., Casadio, M., Morasso, P., & Vergaro, E. (2010). Force-field compensation in a manual tracking task. PLoS ONE, 5, 1–12.CrossRefGoogle Scholar
  165. Strazzulla, I., Nowak, M., Controzzi, M., Cipriani, C., & Castellini, C. (2017). Online bimanual manipulation using surface electromyography and incremental learning. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 25(3), 227–234.CrossRefGoogle Scholar
  166. Stulp, F., Grizou, J., Busch, B., & Lopes, M. (2015). Facilitating intention prediction for humans by optimizing robot motions. In 2015 IEEE/RSJ international conference on intelligent robots and systems (IROS) (pp. 1249–1255).Google Scholar
  167. Tamei, T., & Shibata, T. (2011). Fast reinforcement learning for three-dimensional kinetic human–robot cooperation with an emg-to-activation model. Advanced Robotics, 25(5), 563–580.CrossRefGoogle Scholar
  168. Tan, J. T. C., Duan, F., Zhang, Y., Watanabe, K., Kato, R., & Arai, T. (2009). Human–robot collaboration in cellular manufacturing: Design and development. In 2009 IEEE/RSJ international conference on intelligent robots and systems (pp. 29–34). IEEE.Google Scholar
  169. Tegin, J., & Wikander, J. (2005). Tactile sensing in intelligent robotic manipulation—a review. Industrial Robot: An International Journal, 32(1), 64–70.CrossRefGoogle Scholar
  170. Todorov, E., & Jordan, M. I. (2002). Optimal feedback control as a theory of motor coordination. Nature Neuroscience, 5(11), 1226–1235.CrossRefGoogle Scholar
  171. Tomasello, M. (2009). Why we cooperate. Cambridge: MIT Press.Google Scholar
  172. Tsagarakis, N. G., Caldwell, D. G., Bicchi, A., Negrello, F., Garabini, M., Choi, W., et al. (2016). Walk-man: A high performance humanoid platform for realistic environments. Journal of Field Robotics (JFR), 34, 1225–1259.CrossRefGoogle Scholar
  173. Tsumugiwa, T., Yokogawa, R., & Hara, K. (2002). Variable impedance control with virtual stiffness for human–robot cooperative peg-in-hole task. In 2002 IEEE/RSJ international conference on intelligent robots and systems (IROS) (Vol. 2, pp. 1075–1081).Google Scholar
  174. Tsumugiwa, T., Yokogawa, R., & Hara, K. (2002b). Variable impedance control with virtual stiffness for human–robot cooperative peg-in-hole task. In IEEE/RSJ international conference on intelligent robots and systems, 2002 (Vol. 2, pp. 1075–1081). IEEE.Google Scholar
  175. Ugur, E., Nagai, Y., Celikkanat, H., & Oztop, E. (2015). Parental scaffolding as a bootstrapping mechanism for learning grasp affordances and imitation skills. Robotica, 33(05), 1163–1180.CrossRefGoogle Scholar
  176. Ulrich, I., & Borenstein, J. (2001). The guidecane-applying mobile robot technologies to assist the visually impaired. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, 31(2), 131–136.CrossRefGoogle Scholar
  177. Vanderborght, B., Albu-Schäffer, A., Bicchi, A., Burdet, E., Caldwell, D. G., Carloni, R., et al. (2013a). Variable impedance actuators: A review. Robotics and Autonomous Systems, 61(12), 1601–1614.CrossRefGoogle Scholar
  178. Vanderborght, B., Albu-Schäffer, A., Bicchi, A., Burdet, E., Caldwell, D. G., Carloni, R., et al. (2013b). Variable impedance actuators: A review. Robotics and Autonomous Systems, 61(12), 1601–1614.CrossRefGoogle Scholar
  179. Vogel, J., Castellini, C., & Van Der Smagt, P. (2011). EMG-based teleoperation and manipulation with the DLR LWR-III. In 2011 IEEE/RSJ international conference on intelligent robots and systems (IROS) (pp. 672–678).Google Scholar
  180. Wakita, K., Huang, J., Di, P., Sekiyama, K., & Fukuda, T. (2013). Human-walking-intention-based motion control of an omnidirectional-type cane robot. IEEE/ASME Transactions on Mechatronics, 18(1), 285–296.CrossRefGoogle Scholar
  181. Wang, G., Zhang, X., Zhang, J., & Gruver, W. (1995). Gripping force sensory feedback for a myoelectrically controlled forearm prosthesis. In IEEE international conference on systems, man and cybernetics, 1995. Intelligent systems for the 21st century (Vol. 1, pp. 501–504). IEEE.Google Scholar
  182. Wang, Z., Liu, L., & Zhou, M. (2005). Protocols and applications of ad-hoc robot wireless communication networks: An overview. Future, 10, 20.Google Scholar
  183. Wojtara, T., Uchihara, M., Murayama, H., Shimoda, S., Sakai, S., Fujimoto, H., et al. (2009). Human–robot collaboration in precise positioning of a three-dimensional object. Automatica, 45(2), 333–342.MathSciNetzbMATHCrossRefGoogle Scholar
  184. Yang, C., Ganesh, G., Haddadin, S., Parusel, S., Albu-Schaeffer, A., & Burdet, E. (2011). Human-like adaptation of force and impedance in stable and unstable interactions. IEEE Transactions on Robotics, 27(5), 918–930.CrossRefGoogle Scholar
  185. Yang, C., Liang, P., Ajoudani, A., & Bicchi, A. (2016). Development of a robotic teaching interface for human to human skill transfer. In IEEE international conference on intelligent robots and systems (IROS). IEEE.Google Scholar
  186. Zanchettin, A. M., Bascetta, L., & Rocco, P. (2013a). Acceptability of robotic manipulators in shared working environments through human-like redundancy resolution. Applied Ergonomics, 44(6), 982–989.CrossRefGoogle Scholar
  187. Zanchettin, A. M., Bascetta, L., & Rocco, P. (2013b). Achieving humanlike motion: Resolving redundancy for anthropomorphic industrial manipulators. IEEE Robotics & Automation Magazine, 20(4), 131–138.CrossRefGoogle Scholar
  188. Zanchettin, A. M., & Rocco, P. (2015). Reactive motion planning and control for compliant and constraint-based task execution. In 2015 IEEE international conference on robotics and automation (ICRA) (pp. 2748–2753). IEEE.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  • Arash Ajoudani
    • 1
  • Andrea Maria Zanchettin
    • 2
  • Serena Ivaldi
    • 3
    • 4
  • Alin Albu-Schäffer
    • 5
  • Kazuhiro Kosuge
    • 6
  • Oussama Khatib
    • 7
  1. 1.Human-Robot Interfaces and Physical Interaction Laboratory (HRI²)Istituto Italiano di TecnologiaGenoaItaly
  2. 2.Dipartimento di Elettronica, Informazione e BioingegneriaPolitecnico di MilanoMilanoItaly
  3. 3.INRIA Nancy Grand-EstVillers-les-NancyFrance
  4. 4.Intelligent Autonomous Systems Lab of TU DarmstadtDarmstadtGermany
  5. 5.The Institute of Robotics and MechatronicsGerman Aerospace Center (DLR)CologneGermany
  6. 6.The System Robotics LaboratoryTohoku UniversitySendaiJapan
  7. 7.The Stanford Robotics LaboratoryStanford UniversityStanfordUSA

Personalised recommendations