Skip to main content
SpringerLink
Log in

Human-Humanoid Interaction and Cooperation: a Review

  • Humanoid and Bipedal Robotics (E Yoshida, Section Editor)
  • Published:
Current Robotics Reports Aims and scope Submit manuscript

Abstract

Purpose of Review

Humanoid robots are versatile platforms with the potential to assist humans in several domains, from education to healthcare, from entertainment to the factory of the future. To find their place into our daily life, where complex interactions and collaborations with humans are expected, their social and physical interaction skills need to be further improved.

Recent Findings

The hallmark of humanoids is their anthropomorphic shape, which facilitates the interaction but at the same time increases the expectations of the human in terms of advanced cooperation capabilities. Cooperation with humans requires an appropriate modeling and real-time estimation of the human state and intention. This information is required both at a high level by the cooperative decision-making policy and at a low level by the interaction controller that implements the physical interaction. Real-time constraints induce simplified models that limit the decision capabilities of the robot during cooperation.

Summary

In this article, we review the current achievements in the context of human-humanoid interaction and cooperation. We report on the cognitive and cooperation skills that the robot needs to help humans achieve their goals, and how these high-level skills translate into the robot’s low-level control commands. Finally, we report on the applications of humanoid robots as humans’ companions, co-workers, or avatars.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

References

  1. Johnson M, Shrewsbury B, Bertrand S, Wu T, Duran D, Floyd M, Abeles P, Stephen D, Mertins N, Lesman A, Carff J, Rifenburgh W, Kaveti P, Straatman W, Smith J, Griffioen M, Layton B, De Boer T, Koolen T, Pratt J. Team IHMC’s lessons learned from the DARPA robotics challenge trials. Journal of Field Robotics. 2015;32. https://doi.org/10.1002/rob.21571.

  2. Kheddar A, Caron S, Gergondet P, Comport A, Tanguy A, Ott C, Henze B, Mesesan G, Englsberger J, Roa M A, Wieber P, Chaumette F, Spindler F, Oriolo G, Lanari L, Escande A, Chappellet K, Kanehiro F, Rabaté P. Humanoid robots in aircraft manufacturing: The airbus use cases. IEEE Robot Autom Mag 2019;26(4):30–45. https://doi.org/10.1109/MRA.2019.2943395.

    Article  Google Scholar 

  3. Shigemi S. Asimo and humanoid robot research at honda. Humanoid Robotics: A Reference. In: Goswami A and Vadakkepat P, editors. Dordrecht: Springer Netherlands; 2018. p. 1–36.

  4. Nelson G, Saunders A, Playter R. The petman and atlas robots at boston dynamics. Humanoid Robotics: A Reference. In: Goswami A and Vadakkepat P, editors. Dordrecht: Springer Netherlands; 2019. p. 169–186.

  5. Digit, advanced mobility for the human world [online]. https://www.agilityrobotics.com/robots.

  6. Lesort T, Lomonaco V, Stoian A, Maltoni D, Filliat D, Díaz-Rodríguez N. Continual learning for robotics: Definition, framework, learning strategies, opportunities and challenges. Inf Fusion 2020;58: 52–68. https://doi.org/10.1016/j.inffus.2019.12.004.

    Article  Google Scholar 

  7. Wood R, Baxter P, Belpaeme T. A review of long-term memory in natural and synthetic systems. Adapt Behav 2012;20(2):81–103. https://doi.org/10.1177/1059712311421219.

    Article  Google Scholar 

  8. Sauppé A, Mutlu B. Robot deictics: How gesture and context shape referential communication. 2014 9th ACM/IEEE International Conference on Human-Robot Interaction (HRI); 2014. p. 342–349. https://doi.org/10.1145/2559636.2559657.

  9. Yogeeswaran K, Złotowski J, Livingstone M, Bartneck C, Sumioka H, Ishiguro H. The interactive effects of robot anthropomorphism and robot ability on perceived threat and support for robotics research. J Hum-Robot Interact 2016;5(2):29– 47. https://doi.org/10.5898/JHRI.5.2.Yogeeswaran.

    Article  Google Scholar 

  10. Takayama L, Dooley D, Ju W. Expressing thought: Improving robot readability with animation principles. Proceedings of the 6th International Conference on Human-Robot Interaction, HRI ’11. New York: Association for Computing Machinery; 2011. p. 69–76.

  11. Vinciarelli A, Pantic M, Bourlard H. Social signal processing: Survey of an emerging domain. Image Vis Comput 2009;27(12):1743–1759. https://doi.org/10.1016/j.imavis.2008.11.007.

    Article  Google Scholar 

  12. Breazeal C. Designing sociable robots. Cambridge: MIT Press; 2002. 10.7551/mitpress/2376.001.0001.

    MATH  Google Scholar 

  13. Scassellati B. Theory of mind for a humanoid robot. Auton Robot 2002;12(1):13–24. https://doi.org/10.1023/A:1013298507114.

    Article  MATH  Google Scholar 

  14. Anzalone S M, Boucenna S, Ivaldi S, Chetouani M. Evaluating the engagement with social robots. Int J Soc Robot 2015;7(4):465–478. https://doi.org/10.1007/s12369-015-0298-7.

    Article  Google Scholar 

  15. Thomas F, Johnston O, Thomas F. The illusion of life: Disney animation. New York: Hyperion; 1995.

    Google Scholar 

  16. Bartneck C, Kulić D, Croft E, Zoghbi S. Measurement instruments for the anthropomorphism, animacy, likeability, perceived intelligence, and perceived safety of robots. Int J Soc Robot 2009;1(1):71–81. https://doi.org/10.1007/s12369-008-0001-3.

    Article  Google Scholar 

  17. Syrdal D S, Dautenhahn K, Koay K L, Walters M L. The negative attitudes towards robots scale and reactions to robot behaviour in a live human-robot interaction study. Adaptive and Emergent Behaviour and Complex Systems. SSAISB; 2009. p. 109–115.

  18. Ramirez M, Geffner H. Goal recognition over pomdps: Inferring the intention of a POMDP agent. IJCAI International Joint Conference on Artificial Intelligence; 2011. p. 2009–2014.

  19. Nikolaidis S, Hsu D, Srinivasa S. Human-robot mutual adaptation in collaborative tasks: Models and experiments. Int J Robot Res 2017;36(5-7):618–634. This paper introduces a formalization for mutual adaptation between robot and a human in a collaborative task and shows how the proposed method can outperform precedent solutions in a human-robot team.

    Article  Google Scholar 

  20. Tabrez A, Luebbers M B, Hayes B. A survey of mental modeling techniques in human–robot teaming. Current Robotics Reports. 2020:1–9.

  21. Bestick A, Bajcsy R, Dragan A D. Implicitly Assisting Humans to Choose Good Grasps in Robot to Human Handovers. 2016 International Symposium on Experimental Robotics. Springer International Publishing; 2017. p. 341–354. Series Title: Springer Proceedings in Advanced Robotics.

  22. Kaelbling L P, Littman M L, Cassandra A R. Planning and acting in partially observable stochastic domains. Artif Intell 1998;101(1):99–134.

    Article  MathSciNet  MATH  Google Scholar 

  23. Silver D, Veness J. Monte-carlo planning in large POMDPs. Advances in Neural Information Processing Systems 23. In: Lafferty J D, Williams C K I, Shawe-Taylor J, Zemel R S, and Culotta A, editors. Curran Associates, Inc.; 2010. p. 2164–2172.

  24. Nikolaidis S, Hsu D, Srinivasa S. Human-robot mutual adaptation in collaborative tasks: Models and experiments. Int J Robot Res 2017;36(5-7):618–634.

    Article  Google Scholar 

  25. Li Y, Tee K P, Chan W L, Yan R, Chua Y, Limbu D K. Continuous role adaptation for human–robot shared control. IEEE Trans Robot 2015;31(3):672–681.

    Article  Google Scholar 

  26. Amodei D, Olah C, Steinhardt J, Christiano P F, Schulman J, Mané D. 2016. Concrete problems in ai safety. arXiv:1606.06565.

  27. Romano F, Nava G, Azad M, Čamernik J, Dafarra S, Dermy O, Latella C, Lazzaroni M, Lober R, Lorenzini M, et al. The codyco project achievements and beyond: Toward human aware whole-body controllers for physical human robot interaction. IEEE Robot Autom Lett 2017;3(1):516–523.

    Article  Google Scholar 

  28. Otani K, Bouyarmane K, Ivaldi S. Generating assistive humanoid motions for co-manipulation tasks with a multi-robot quadratic program controller. 2018 IEEE International Conference on Robotics and Automation (ICRA); 2018. p. 3107–3113. This paper presents a multi-robot quadratic program controller which allows to keep the robot balanced, while also assisting the human in achieving their shared objectives.

  29. Dermy O, Chaveroche M, Colas F, Charpillet F, Ivaldi S. Prediction of human whole-body movements with AE-ProMPs. 2018 IEEE-RAS 18th International Conference on Humanoid Robots (Humanoids); 2018. p. 572–579.

  30. Penco L, Scianca N, Modugno V, Lanari L, Oriolo G, Ivaldi S. A multimode teleoperation framework for humanoid loco-manipulation: An application for the icub robot. IEEE Robot Autom Mag 2019;26(4):73–82.

    Article  Google Scholar 

  31. Tirupachuri Y, Nava G, Rapetti L, Latella C, Pucci D. Trajectory advancement during human-robot collaboration. 2019 28th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN); 2019. p. 1–8.

  32. Gazar A, Nava G, Chavez F J A, Pucci D. Jerk control of floating base systems with contact-stable parameterized force feedback. IEEE Trans Robot. 2020.

  33. Brygo A, Sarakoglou I, Tsagarakis N, Caldwell D. Tele-manipulation with a humanoid robot under autonomous joint impedance regulation and vibrotactile balancing feedback; 2014. https://doi.org/10.1109/HUMANOIDS.2014.7041465.

  34. Ranatunga I, Lewis F L, Popa D O, Tousif S M. Adaptive admittance control for human–robot interaction using model reference design and adaptive inverse filtering. IEEE Trans Control Syst Technol 2016;25(1):278–285.

    Article  MATH  Google Scholar 

  35. Kormushev P, Nenchev D N, Calinon S, Caldwell D G. Upper-body kinesthetic teaching of a free-standing humanoid robot. 2011 IEEE International Conference on Robotics and Automation; 2011. p. 3970–3975.

  36. Bussy A, Gergondet P, Kheddar A, Keith F, Crosnier A. Proactive behavior of a humanoid robot in a haptic transportation task with a human partner. 2012 IEEE RO-MAN: The 21st IEEE International Symposium on Robot and Human Interactive Communication; 2012. p. 962–967.

  37. Mainprice J, Sisbot E A, Jaillet L, Cortés J, Alami R, Siméon T. Planning human-aware motions using a sampling-based costmap planner. 2011 IEEE International Conference on Robotics and Automation; 2011. p. 5012–5017.

  38. Li Y, Ge S S. Human–robot collaboration based on motion intention estimation. IEEE/ASME Trans Mechatron 2013;19(3):1007–1014.

    Article  Google Scholar 

  39. Jarrasse N, Sanguineti V, Burdet E. Slaves no longer: review on role assignment for human–robot joint motor action. Adapt Behav 2014;22(1):70–82.

    Article  Google Scholar 

  40. Buondonno G, Patota F, Wang H, De Luca A, Kosuge K. A model predictive control approach for the partner ballroom dance robot. 2015 IEEE International Conference on Robotics and Automation (ICRA); 2015. p. 774–780.

  41. Vasalya A. 2019. Human and Humanoid robot co-workers: motor contagions and whole-body handover. PhD thesis, Université de Montpellier. https://hal.archives-ouvertes.fr/tel-02839897.

  42. Zheng C, Wu W, Yang T, Zhu S, Chen C, Liu R, Shen J, Kehtarnavaz N, Shah M. 2020. Deep learning-based human pose estimation: A survey.

  43. Latella C, Lorenzini M, Lazzaroni M, Romano F, Traversaro S, Akhras M A, Pucci D, Nori F. Towards real-time whole-body human dynamics estimation through probabilistic sensor fusion algorithms. Auton Robot 2019;43(6):1591–1603. The authors proposed a probabilistic framework and an estimation tool for online monitoring of the human dynamics during human-robot collaboration tasks.

    Article  Google Scholar 

  44. Lorenzini M, Kim W, De Momi E, Ajoudani A. A synergistic approach to the real-time estimation of the feet ground reaction forces and centers of pressure in humans with application to human–robot collaboration. IEEE Robot Autom Lett 2018;3(4):3654–3661.

    Article  Google Scholar 

  45. Sorrentino I, Andrade Chavez F J, Latella C, Fiorio L, Traversaro S, Rapetti L, Tirupachuri Y, Guedelha N, Maggiali M, Dussoni S, et al. A novel sensorised insole for sensing feet pressure distributions. Sensors 2020;20(3):747.

    Article  Google Scholar 

  46. Agravante D J, Cherubini A, Sherikov A, Wieber P-B, Kheddar A. Human-humanoid collaborative carrying. IEEE Trans Robot 2019;35(4):833–846. This paper presents a framework for collaborative carrying based on whole-body controlling, the framework considers the taxonomy of the task, the roles of the agent, the walking pattern and the stabilization in presence of external forces.

    Article  Google Scholar 

  47. Peternel L, Tsagarakis N, Caldwell D, Ajoudani A. Adaptation of robot physical behaviour to human fatigue in human-robot co-manipulation. 2016 IEEE-RAS 16th International Conference on Humanoid Robots (Humanoids); 2016. p. 489–494.

  48. Ison M, Vujaklija I, Whitsell B, Farina D, Artemiadis P. Simultaneous myoelectric control of a robot arm using muscle synergy-inspired inputs from high-density electrode grids. 2015 IEEE International Conference on Robotics and Automation (ICRA); 2015. p. 6469–6474.

  49. Li W, Jaramillo C, Li Y. Development of mind control system for humanoid robot through a brain computer interface. 2012 Second International Conference on Intelligent System Design and Engineering Application; 2012. p. 679–682.

  50. Bell C J, Shenoy P, Chalodhorn R, Rao RPN. Control of a humanoid robot by a noninvasive brain–computer interface in humans. J Neural Eng 2008;5(2):214.

    Article  Google Scholar 

  51. Bossi F, Willemse C, Cavazza J, Marchesi S, Murino V, Wykowska A. The human brain reveals resting state activity patterns that are predictive of biases in attitudes toward robots. Sci Robot. 2020;5(46). https://robotics.sciencemag.org/content/5/46/eabb6652.full.pdf, https://doi.org/10.1126/scirobotics.abb6652.

  52. Zhou T, Cha J S, Gonzalez G, Wachs J P, Sundaram C P, Yu D. Multimodal physiological signals for workload prediction in robot-assisted surgery. ACM Trans Human-Robot Interact (THRI) 2020;9(2): 1–26.

    Article  Google Scholar 

  53. Hu Y, Benallegue M, Venture G, Yoshida E. Interact with me: An exploratory study on interaction factors for active physical human-robot interaction. IEEE Robot Autom Lett 2020;5(4): 6764–6771. https://doi.org/10.1109/LRA.2020.3017475.

    Article  Google Scholar 

  54. Anzalone S M, Boucenna S, Ivaldi S, Chetouani M. Evaluating the engagement with social robots. Int J Soc Robot 2015;7(4):465–478.

    Article  Google Scholar 

  55. Baraglia J, Cakmak M, Nagai Y, Rao R, Asada M. Efficient human-robot collaboration: when should a robot take initiative? Int J Robot Res. 2017:027836491668825. https://doi.org/10.1177/0278364916688253.

  56. Risskov Sørensen A, Palinko O, Krüger N. Classification of visual interest based on gaze and facial features for human-robot interaction. Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications. SCITEPRESS Digital Library; 2020.

  57. Cangelosi A, Ogata T. In: Goswami A, Vadakkepat P, editors. Speech and language in humanoid robots. Dordrecht: Springer Netherlands; 2016.

  58. Cruz-Maya A, Agrigoroaie R, Tapus A. Improving user’s performance by motivation: Matching robot interaction strategy with user’s regulatory state. International Conference on Social Robotics, Springer; 2017. p. 464–473.

  59. Vasalya A, Ganesh G, Kheddar A. More than just co-workers: Presence of humanoid robot co-worker influences human performance. PLOS ONE 2018;13(11):1–19. https://doi.org/10.1371/journal.pone.0206698.

    Article  Google Scholar 

  60. Kamide H, Mae Y, Kawabe K, Shigemi S, Hirose M, Arai T. New measurement of psychological safety for humanoid. 2012 7th ACM/IEEE International Conference on Human-Robot Interaction (HRI); 2012. p. 49–56.

  61. Scataglini S, Paul G. Dhm and posturography: Academic Press; 2019.

  62. Maurice P, Padois V, Measson Y, Bidaud P. Human-oriented design of collaborative robots. Int J Ind Ergon 2017;57:88–102.

    Article  Google Scholar 

  63. Peternel L, Fang C, Tsagarakis N, Ajoudani A. A selective muscle fatigue management approach to ergonomic human-robot co-manipulation. Robot Comput Integr Manuf 2019;58:69–79.

    Article  Google Scholar 

  64. Wang H, Kosuge K. Control of a robot dancer for enhancing haptic human-robot interaction in waltz. IEEE Trans Haptics 2012;5(3):264–273.

    Article  Google Scholar 

  65. Kobayashi T, Dean-Leon E, Guadarrama-Olvera J R, Bergner F, Cheng G. Multi-contacts force-reactive walking control during physical human-humanoid interaction. 2019 IEEE-RAS 19th International Conference on Humanoid Robots (Humanoids); 2019. p. 33–39. This paper proposes a force-reactive walking control framework for stabilization during physical human-robot interaction where the contact forces are measured by robotic skin. The method has been tested on dancing task while teaching footsteps.

  66. Granados D F P, Yamamoto B A, Kamide H, Kinugawa J, Kosuge K. Dance teaching by a robot: Combining cognitive and physical human–robot interaction for supporting the skill learning process. IEEE Robot Autom Lett 2017;2(3):1452–1459.

    Article  Google Scholar 

  67. Ikemoto S, Amor H B, Minato T, Ishiguro H, Jung B. Physical interaction learning: Behavior adaptation in cooperative human-robot tasks involving physical contact. RO-MAN 2009-The 18th IEEE International Symposium on Robot and Human Interactive Communication; 2009. p. 504–509.

  68. López A M, Vaillant J, Keith F, Fraisse P, Kheddar A. Compliant control of a humanoid robot helping a person stand up from a seated position. 2014 IEEE-RAS International Conference on Humanoid Robots; 2014. p. 817–822. This paper proposes a whole-body control framework to plan a stable initial posture for a humanoid robot supporting a person from sitting to standing while considering the patience degree of autonomy. Moreover the authors proposed a control law to make the robot keep a contact force and follow the motion of the person compliantly.

  69. Bolotnikova A, Courtois S, Kheddar A. Autonomous initiation of human physical assistance by a humanoid. 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN); 2020. p. 857–862. Framework for physical assistance of a frail person based on whole body controller for autonomously reach a person, perform audiovisual communication of intent, and establish several physical contacts.

  70. Mukai T, Hirano S, Yoshida M, Nakashima H, Guo S, Hayakawa Y. Tactile-based motion adjustment for the nursing-care assistant robot riba. 2011 IEEE International Conference on Robotics and Automation; 2011. p. 5435–5441.

  71. Stückler J, Behnke S. Following human guidance to cooperatively carry a large object. 2011 11th IEEE-RAS International Conference on Humanoid Robots; 2011. p. 218–223.

  72. Lanini J, Razavi H, Urain J, Ijspeert A. Human intention detection as a multiclass classification problem: Application in physical human–robot interaction while walking. IEEE Robot Autom Lett 2018; 3(4):4171–4178.

    Article  Google Scholar 

  73. Asfour T, Waechter M, Kaul L, Rader S, Weiner P, Ottenhaus S, Grimm R, Zhou Y, Grotz M, Paus F. Armar-6: A high- performance humanoid for human-robot collaboration in real-world scenarios. IEEE Robot Autom Mag 2019;26(4):108–121.

    Article  Google Scholar 

  74. Bombile M, Billard A. Capture-point based balance and reactive omnidirectional walking controller. 2017 IEEE-RAS 17th International Conference on Humanoid Robotics (Humanoids); 2017. p. 17–24.

  75. Stasse O, Evrard P, Perrin N, Mansard N, Kheddar A. Fast foot prints re-planning and motion generation during walking in physical human-humanoid interaction. 2009 9th IEEE-RAS International Conference on Humanoid Robots; 2009. p. 284–289.

  76. Evrard P, Gribovskaya E, Calinon S, Billard A, Kheddar A. Teaching physical collaborative tasks: object-lifting case study with a humanoid. 2009 9th IEEE-RAS International Conference on Humanoid Robots; 2009. p. 399–404.

  77. Calinon S, Guenter F, Billard A. On learning, representing, and generalizing a task in a humanoid robot. IEEE Trans Syst Man Cybern Part B (Cybern) 2007;37(2):286–298.

    Article  Google Scholar 

  78. Lee D, Ott C, Nakamura Y, Hirzinger G. Physical human robot interaction in imitation learning. 2011 IEEE International Conference on Robotics and Automation; 2011. p. 3439–3440.

  79. Jorgensen S J, Lanighan M W, Bertrand S S, Watson A, Altemus J S, Askew R S, Bridgwater L, Domingue B, Kendrick C, Lee J, et al. Deploying the nasa valkyrie humanoid for ied response: An initial approach and evaluation summary. 2019 IEEE-RAS 19th International Conference on Humanoid Robots (Humanoids); 2019. https://doi.org/10.1109/humanoids43949.2019.9034993.

  80. Tachi S. Telexistence, 2nd ed.: World Scientific; 2015.

  81. Gitai partners with JAXA to send telepresence robots to space [online]. https://spectrum.ieee.org/automaton/robotics/space-robots/gitai-partners-with-jaxa-to-send-telepresence-robots-to-spacehttps://spectrum.ieee.org/automaton/robotics/space-robots/gitai-partners-with-jaxa-to-send-telepresence-robots-to-space.

  82. Ramos O E, Mansard N, Stasse O, Benazeth C, Hak S, Saab L. Dancing humanoid robots: Systematic use of osid to compute dynamically consistent movements following a motion capture pattern. IEEE Robot Autom Mag 2015;22(4):16–26. https://doi.org/10.1109/MRA.2015.2415048.

    Article  Google Scholar 

  83. Hamamsy L E, Johal W, Asselborn T, Nasir J, Dillenbourg P. Learning by collaborative teaching: An engaging multi-party cowriter activity. 2019 28th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN); 2019. p. 1–8. https://doi.org/10.1109/RO-MAN46459.2019.8956358.

  84. Chang C-W, Lee J-H, Chao P-Y, Wang C-Y, Chen G-D. Exploring the possibility of using humanoid robots as instructional tools for teaching a second language in primary school. J Educ Technol Soc 2010;13(2):13–24. http://www.jstor.org/stable/jeductechsoci.13.2.13.

    Google Scholar 

  85. Wong C J, Tay Y L, Wang R, Wu Y. Human-robot partnership: A study on collaborative storytelling. 2016 11th ACM/IEEE International Conference on Human-Robot Interaction (HRI); 2016. p. 535–536. https://doi.org/10.1109/HRI.2016.7451843.

  86. Görer B, Salah A A, Akın H L. A robotic fitness coach for the elderly. Ambient Intelligence. In: Augusto J C, Wichert R, Collier R, Keyson D, Salah A A, and Tan A-H, editors. Cham: Springer International Publishing; 2013. p. 124–139.

  87. Robinson N L, Connolly J, Hides L, Kavanagh D J. Social robots as treatment agents: Pilot randomized controlled trial to deliver a behavior change intervention. Internet Intervent 2020;21:100320. https://doi.org/10.1016/j.invent.2020.100320.

    Article  Google Scholar 

  88. Lau Y, Chee D G H, Chow X P, Wong S H, Cheng L J, Lau S T. Humanoid robot-assisted interventions among children with diabetes: A systematic scoping review. Int J Nurs Stud 2020;111:103749. https://doi.org/10.1016/j.ijnurstu.2020.103749.

    Article  Google Scholar 

  89. Pennisi P, Tonacci A, Tartarisco G, Billeci L, Ruta L, Gangemi S, Pioggia G. Autism and social robotics: A systematic review. Autism Res 2016;9(2):165–183. https://doi.org/10.1002/aur.1527.

    Article  Google Scholar 

  90. Kim W, Balatti P, Lamon E, Ajoudani A. Moca-man: A mobile and reconfigurable collaborative robot assistant for conjoined human-robot actions. 2020 IEEE International Conference on Robotics and Automation (ICRA); 2020. p. 10191–10197.

  91. Yokoyama K, Handa H, Isozumi T, Fukase Y, Kaneko K, Kanehiro F, Kawai Y, Tomita F, Hirukawa H. Cooperative works by a human and a humanoid robot. 2003 IEEE International Conference on Robotics and Automation (Cat. No. 03CH37422); 2003. p. 2985–2991.

  92. Kim W, Lorenzini M, Balatti P, Wu Y, Ajoudani A. Towards ergonomic control of collaborative effort in multi-human mobile-robot teams. 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS); 2019. p. 3005–3011.

  93. Tirupachuri Y, Nava G, Ferigo D, Tagliapietra L, Latella C, Nori F, Pucci D. Towards partner-aware humanoid robot control under physical interactions. IntelliSys; 2019.

  94. Bolotnikova A, Courtois S, Kheddar A. Autonomous initiation of human physical assistance by a humanoid. 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN); 2020. p. 857–862.

  95. Abi-Farrajl F, Henze B, Werner A, Panzirsch M, Ott C, Roa M A. Humanoid teleoperation using task-relevant haptic feedback. 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS); 2018. p. 5010–5017.

  96. Ishiguro Y, Makabe T, Nagamatsu Y, Kojio Y, Kojima K, Sugai F, Kakiuchi Y, Okada K, Inaba M. Bilateral humanoid teleoperation system using whole-body exoskeleton cockpit TABLIS. IEEE Robot Autom Lett 2020;5(4):6419–6426.

    Article  Google Scholar 

  97. Ishiguro Y, Kojima K, Sugai F, Nozawa S, Kakiuchi Y, Okada K, Inaba M. High speed whole body dynamic motion experiment with real time master-slave humanoid robot system. 2018 IEEE International Conference on Robotics and Automation (ICRA); 2018. p. 1–7. This paper proposes a whole body master-slave control technique for online teleoperation of a life-sized humanoid robot.

  98. Villegas R, Yang J, Ceylan D, Lee H. Neural kinematic networks for unsupervised motion retargetting. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition; 2018. p. 8639–8648.

  99. Englsberger J, Werner A, Ott C, Henze B, Roa M A, Garofalo G, Burger R, Beyer A, Eiberger O, Schmid K, et al. Overview of the torque-controlled humanoid robot toro. 2014 IEEE-RAS International Conference on Humanoid Robots; 2014. p. 916–923.

  100. Brygo A, Sarakoglou I, Garcia-Hernandez N, Tsagarakis N. Humanoid robot teleoperation with vibrotactile based balancing feedback. Haptics: Neuroscience, Devices, Modeling, and Applications. In: Auvray M and Duriez C, editors. Berlin: Springer; 2014. p. 266–275.

Download references

Funding

This work was supported by the European Union Horizon 2020 Research and Innovation Program under Grant Agreement No. 731540 (project AnDy), the European Research Council (ERC) under Grant Agreement No. 637972 (project ResiBots), the French Agency for Research under the ANR Grants No. ANR-18-CE33-0001 (project Flying Co-Worker) and ANR-20-CE33-0004 (project ROOIBOS), the ANR-FNS Grant No. ANR-19-CE19-0029 - FNS 200021E_189475/1 (project iReCheck), the CHIST-ERA grant HEAP (CHIST-ERA-17-ORMR-003), the Inria-DGA grant (“humanoïdeïde résilient”ésilient”), and the Inria “ADT” wbCub/wbTorque.

Author information

Author notes

    Authors and Affiliations

    1. Inria, Loria, Université de Lorraine, CNRS, Nancy, F-54000, France

      Lorenzo Vianello, Luigi Penco, Waldez Gomes, Yang You, Pauline Maurice, Vincent Thomas & Serena Ivaldi

    2. CRAN, Nancy, F-54000, France

      Lorenzo Vianello

    3. Laboratoire CHArt, Université Paris 8, Paris, F-93200, France

      Salvatore Maria Anzalone

    Authors
    1. Lorenzo Vianello
      View author publications

      You can also search for this author in PubMed Google Scholar

    2. Luigi Penco
      View author publications

      You can also search for this author in PubMed Google Scholar

    3. Waldez Gomes
      View author publications

      You can also search for this author in PubMed Google Scholar

    4. Yang You
      View author publications

      You can also search for this author in PubMed Google Scholar

    5. Salvatore Maria Anzalone
      View author publications

      You can also search for this author in PubMed Google Scholar

    6. Pauline Maurice
      View author publications

      You can also search for this author in PubMed Google Scholar

    7. Vincent Thomas
      View author publications

      You can also search for this author in PubMed Google Scholar

    8. Serena Ivaldi
      View author publications

      You can also search for this author in PubMed Google Scholar

    Corresponding author

    Correspondence to Serena Ivaldi.

    Ethics declarations

    Conflict of Interest

    The authors declare no competing interests.

    Additional information

    Human and Animal Rights and Informed Consent

    This article does not contain any studies with human or animal subjects performed by any of the authors.

    Publisher’s note

    Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

    These authors contributed equally to this work.

    This article is part of the Topical Collection on Humanoid and Bipedal Robotics

    Rights and permissions

    Reprints and permissions

    About this article

    Check for updates. Verify currency and authenticity via CrossMark

    Cite this article

    Vianello, L., Penco, L., Gomes, W. et al. Human-Humanoid Interaction and Cooperation: a Review. Curr Robot Rep 2, 441–454 (2021). https://doi.org/10.1007/s43154-021-00068-z

    Download citation

    • Accepted:

    • Published:

    • Issue Date:

    • DOI: https://doi.org/10.1007/s43154-021-00068-z

    Keywords

    Search

    Navigation