Nothing Special   »   [go: up one dir, main page]
Skip to main content
Springer Nature Link
Log in

Robot Inference of Human States: Performance and Transparency in Physical Collaboration

  • Chapter
  • First Online:
The 21st Century Industrial Robot: When Tools Become Collaborators

Part of the book series: Intelligent Systems, Control and Automation: Science and Engineering ((ISCA,volume 81))

  • 1073 Accesses

Abstract

To flexibly collaborate towards a shared goal in human–robot interaction (HRI), a robot must appropriately respond to changes in their human partner. The robot can realize this flexibility by responding to certain inputs, or by inferring some aspect of their collaborator and using this to modify robot behavior—approaches which reflect design viewpoints of robots as tools and collaborators, respectively. Independent of this design viewpoint, the robot’s response to a change in collaborator state must also be designed. In this regard, HRI approaches can be distinguished according to the scope of their design objectives: whether the design goal depends on the behavior of the individual agents or the coupled team. This chapter synthesizes work on physical HRI, largely in manufacturing tasks, according to the design viewpoint and scope of objective used. HRI is posed as the coupling of two dynamic systems; a framework which allows a unified presentation of the various design approaches and, within which, common concepts in HRI can be posed (intent, authority, information flow). Special attention is paid to predictability at various stages of the design and deployment process: whether the designer can predict team performance, whether the human can predict robot behavior, and to what degree the human behavior can be modelled or learned.

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 820689—SHERLOCK.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Tsarouchi P, Makris S, Chryssolouris G, Human–robot interaction review and challenges on task planning and programming 29(8):916–931

    Google Scholar 

  2. Krüger J, Lien TK, Verl A, Cooperation of human and machines in assembly lines 58(2):628–646

    Google Scholar 

  3. Bastidas-Cruz A, Heimann O, Haninger K, Krüger J, Information requirements and interfaces for the programming of robots in flexible manufacturing

    Google Scholar 

  4. Willems JC, The behavioral approach to open and interconnected systems 27(6):46–99

    Google Scholar 

  5. Hoffman G, Evaluating fluency in human–robot collaboration 49(3):209–218

    Google Scholar 

  6. Losey DP, McDonald CG, Battaglia E, O’Malley MK, A review of intent detection, arbitration, and communication aspects of shared control for physical human–robot interaction 70(1):010804

    Google Scholar 

  7. Ajoudani A, Zanchettin AM, Ivaldi S, Albu-Schãffer A, Kosuge K, Khatib O, Progress and prospects of the human–robot collaboration 1–19

    Google Scholar 

  8. Demiris Y, Prediction of intent in robotics and multi-agent systems 8(3):151–158

    Google Scholar 

  9. Duchaine V, Gosselin CM, General model of human-robot cooperation using a novel velocity based variable impedance control. In: EuroHaptics conference, 2007 and symposium on haptic interfaces for virtual environment and teleoperator systems. World Haptics 2007, Second 3oint, IEEE, pp 446–451

    Google Scholar 

  10. Paynter HM, Analysis and design of engineering systems: class notes for M.I.T. course 2,751. M.I.T. Press

    Google Scholar 

  11. Hogan N, Impedance control: an approach to manipulation. In: American control conference, 1984, IEEE, pp 304–313

    Google Scholar 

  12. Khan SG, Herrmann G, Al Grafi M, Pipe T, Melhuish C, Compliance control and human–robot interaction: part 1—survey, 11(03):1430001

    Google Scholar 

  13. Haddadin S, Albu-Schaffer A, Frommberger M, Rossmann J, Hirzinger G, The “dlr crash report”: towards a standard crash-testing protocol for robot safety-part ii: discussions. In: IEEE international conference on robotics and automation, 2009. ICRA ’09, IEEE, pp 280–287

    Google Scholar 

  14. Haninger K, Surdilovic D, Bounded collision force by the Sobolev norm: compliance and control for interactive robots. In: 2019 IEEE international conference on robotics and automation (ICRA), pp 8259–8535

    Google Scholar 

  15. ÂAström KJ, Murray RM, Feedback systems: an introduction for scientists and engineers. Princeton University Press

    Google Scholar 

  16. Roth E, Howell D, Beckwith C, Burden SA, Toward experimental validation of a model for human sensorimotor learning and control in teleoperation 101941X

    Google Scholar 

  17. Conditt MA, Mussa-Ivaldi FA, Central representation of time during motor learning 96(20):11625–11630

    Google Scholar 

  18. Burdet E, Tee KP, Mareels I, Milner TE, Chew CM, Franklin DW, Osu R, Kawato M, Stability and motor adaptation in human arm movements 94(1):20–32

    Google Scholar 

  19. Nikolaidis S, Nath S, Procaccia AD, Srinivasa S, Game-theoretic modeling of human adaptation in human-robot collaboration. In: Proceedings of the 2017 ACM/IEEE international conference on human-robot interaction, ACM, pp 323–331

    Google Scholar 

  20. Yanco HA, Drury JL, Scholtz J, Beyond usability evaluation: analysis of human-robot interaction at a major robotics competition 19(1–2):117–149

    Google Scholar 

  21. Roncone A, Mangin O, Scassellati B, Transparent role assignment and task allocation in human robot collaboration. In: 2017 IEEE international conference on robotics and automation (ICRA), IEEE, pp 1014–1021

    Google Scholar 

  22. Eyssel F, Kuchenbrandt D, Bobinger S, Effects of anticipated human-robot interaction and predictability of robot behavior on perceptions of anthropomorphism. In: Proceedings of the 6th international conference on human-robot interaction, pp 61–68

    Google Scholar 

  23. Dragan AD, Srinivasa SS, A policy-blending formalism for shared control 32(7):790–805

    Google Scholar 

  24. Li Y, Ge SS, Human–robot collaboration based on motion intention estimation 19(3):1007–1014

    Google Scholar 

  25. Wang C, Li Y, Ge SS, Lee TH, Reference adaptation for robots in physical interactions with unknown environments 47(11):3504–3515

    Google Scholar 

  26. Kang G, Oh HS, JKSeo HS, Kim U, Choi HR, Variable admittance control of robot manipulators based on human intention 24(3):1023–1032

    Google Scholar 

  27. Peternel L, Tsagarakis N, Caldwell D, Ajoudani A, Robot adaptation to human physical fatigue in human–robot co-manipulation, pp 1–11

    Google Scholar 

  28. Gopinathan S, Otting S, Steil J, A user study on personalized adaptive stiffness control modes for human-robot interaction. In: The 26th IEEE international symposium on robot and human interactive communication, pp 831–837

    Google Scholar 

  29. Khoramshahi M, Billard A, A dynamical system approach to task-adaptation in physical human–robot interaction 43(4):927–946

    Google Scholar 

  30. Rani P, Sarkar N, Smith CA, Kirby LD, Anxiety detecting robotic system–towards implicit human-robot collaboration 22(1):85–95

    Google Scholar 

  31. Sadigh D, Sastry SS, Seshia SA, Dragan A, Information gathering actions over human internal state. In: 2016 IEEE/RS3 international conference on intelligent robots and systems (IROS), IEEE, pp 66–73

    Google Scholar 

  32. Kanno T, Nakata K, Furuta K, A method for team intention inference 58(4):393–413

    Google Scholar 

  33. Peternel L, Tsagarakis N, Ajoudani A, Towards multi-modal intention interfaces for human-robot co-manipulation. In: Proceedings 2016 IEEE/RS3 international conference on intelligent robots and systems (IROS)

    Google Scholar 

  34. Kaneishi D, Matthew RP, Tomizuka M, A sEMG classification framework with less training data. In: 2018 40th annual international conference of the IEEE engineering in medicine and biology society (EMBC), IEEE, pp 1680–1684

    Google Scholar 

  35. Gomi H, Kawato M, Human arm stiffness and equilibrium-point trajectory during multi-joint movement 76(3):163–171

    Google Scholar 

  36. Medina JR, Endo S, Hirche S, Impedance-based Gaussian Processes for predicting human behavior during physical interaction. In: 2016 IEEE international conference on robotics and automation (ICRA), IEEE, pp 3055–3061

    Google Scholar 

  37. Erden MS, Billard A, End-point impedance measurements at human hand during interactive manual welding with robot. In: 2014 IEEE international conference on robotics and automation (ICRA), IEEE, pp 126–133

    Google Scholar 

  38. Tsumugiwa T, Yokogawa R, Hara K, Variable impedance control based on estimation of human arm stiffness for human-robot cooperative calligraphic task. In: IEEE international conference on robotics and automation, 2002. Proceedings. ICRA ’02 1, IEEE, pp 644–650

    Google Scholar 

  39. Haninger K, Surdilovic D, Identification of human dynamics in user-led physical human robot environment interaction. In: 2018 27th international symposium on robot and human interactive communication (RO-MAN), pp 509–514

    Google Scholar 

  40. Wang Z, Wang B, Liu H, Kong Z, Recurrent convolutional networks based intention recognition for human-robot collaboration tasks. In: 2017 IEEE international conference on systems, man, and cybernetics (SMC), IEEE, pp 1675–1680

    Google Scholar 

  41. Wang Z, Mülling K, Deisenroth MP, Ben Amor H, Vogt D, Schölkopf B, Peters J, Probabilistic movement modeling for intention inference in human–robot interaction 32(7):841–858

    Google Scholar 

  42. Takagi A, Ganesh G, Yoshioka T, Kawato M, Burdet E, Physically interacting individuals estimate the partner’s goal to enhance their movements 1(3):0054

    Google Scholar 

  43. Sunberg ZN, Ho CJ, Kochenderfer MJ, The value of inferring the internal state of traffic participants for autonomous freeway driving. In: 2017 American control conference (ACC), pp 3004–3010

    Google Scholar 

  44. Albrecht SV, Stone P, Autonomous agents modelling other agents: a comprehensive survey and open problems 258:66–95

    Google Scholar 

  45. Choudhury R, Swamy G, Hadfield-Menell D, Dragan A, On the utility of model learning in HRI

    Google Scholar 

  46. Villani V, Pini F, Leali F, Secchi C, Survey on human–robot collaboration in industrial settings: Safety, intuitive interfaces and applications 55:248–266

    Google Scholar 

  47. Cherubini A, Passama R, Crosnier A, Lasnier A, Fraisse P, Collaborative manufacturing with physical human–robot interaction 40:1–13

    Google Scholar 

  48. Medina JR, Lorenz T, Hirche S, Considering human behavior uncertainty and disagreements in human–robot cooperative manipulation. In: Wang Y, Zhang F (eds) Trends in control and decision-making for human–robot collaboration systems, pp 207–240, Springer International Publishing

    Google Scholar 

  49. Augustsson S, Olsson J, Christiernin LG, Bolmsjö G, How to transfer information between collaborating human operators and industrial robots in an assembly. In: Proceedings of the 8th nordic conference on human-computer interaction: fun, fast, foundational, pp 286–294

    Google Scholar 

  50. Michalos G, Kousi N, Karagiannis P, Gkournelos C, Dimoulas K, Koukas S, Mparis K, Papavasileiou A, Makris S, Seamless human robot collaborative assembly—an automotive case study 55:194–211

    Google Scholar 

  51. Fridovich-Keil D, Bajcsy A, Fisac JF, Herbert SL, Wang S, Dragan AD, Tomlin CJ, Confidence-aware motion prediction for real-time collision avoidance 1:0278364919859436

    Google Scholar 

  52. Ranatunga I, Cremer S, Popa DO, Lewis FL, Intent aware adaptive admittance control for physical Human-Robot Interaction. In: 2015 IEEE international conference on robotics and automation (ICRA), IEEE, pp 5635–5640

    Google Scholar 

  53. Dimeas F, Aspragathos N, Reinforcement learning of variable admittance control for human-robot co-manipulation. In 2015 IEEE/RSJ international conference on intelligent robots and systems (IROS), IEEE, pp 1011–1016

    Google Scholar 

  54. Lawitzky M, Kimmel M, Ritzer P, Hirche S, Trajectory generation under the least action principle for physical human-robot cooperation. In: 2013 IEEE international conference on robotics and automation, IEEE, pp 4285–4290

    Google Scholar 

  55. Ranatunga I, Lewis FL, Popa DO, Tousif SM, Adaptive admittance control for human-robot interaction using model reference design and adaptive inverse filtering, 25(1):278–285

    Google Scholar 

  56. Modares H, Ranatunga I, Lewis FL, Popa DO, Optimized assistive human–robot interaction using reinforcement learning 46(3):655–667

    Google Scholar 

  57. Dragan AD, Robot planning with mathematical models of human state and action

    Google Scholar 

  58. Li Y, Carboni G, Gonzalez F, Campolo D, Burdet E, Differential game theory for versatile physical human–robot interaction 1(1):36

    Google Scholar 

  59. Brooks C, Szafir D, Building second-order mental models for human-robot interaction

    Google Scholar 

  60. Munzer T, Toussaint M, Lopes M, Preference learning on the execution of collaborative human-robot tasks. In: 2017 IEEE international conference on robotics and automation (ICRA), IEEE, pp 879–885

    Google Scholar 

  61. Mutlu B, Roy N, Săbanovié S, Cognitive human–robot interaction. In: Springer handbook of robotics, Springer, pp 1907–1934

    Google Scholar 

  62. Castro VF, Clodic A, Alami R, Pacherie E, Commitments in human-robot interaction

    Google Scholar 

  63. Unhelkar VV, Yang XJ, Shah JA, Challenges for communication decision-making in sequential human-robot collaborative tasks. In: Workshop on mathematical models, algorithms, and human-robot interaction at R:SS

    Google Scholar 

  64. Medina JR, Lorenz T, Lee D, Hirche S, Disagreement-aware physical assistance through risk-sensitive optimal feedback control. In: 2012 IEEE/RSJ international conference on intelligent robots and systems, IEEE, pp 3639–3645

    Google Scholar 

  65. Reddy S, Dragan AD, Levine S, Shared autonomy via deep reinforcement learning

    Google Scholar 

  66. Javdani S, Srinivasa SS, Bagnell JA, Shared autonomy via Hindsight optimization

    Google Scholar 

  67. Gomes W, Lizarralde F, Role adaptive admittance controller for human-robot co-manipulation

    Google Scholar 

  68. Rozo L, Calinon S, Caldwell DG, Jimenez P, Torras C, Learning physical collaborative robot behaviors from human demonstrations 32(3):513–527

    Google Scholar 

  69. Arumugam D, Lee JK, Saskin S, Littman ML, Deep reinforcement learning from policy-dependent human feedback

    Google Scholar 

  70. Hadfield-Menell D, Russell SJ, Abbeel P, Dragan A, Cooperative inverse reinforcement learning. In: Advances in neural information processing systems, pp 3909–3917

    Google Scholar 

  71. Nemec B, Likar N, Gams A, Ude A, Human robot cooperation with compliance adaptation along the motion trajectory, 42(5):1023–1035

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Fraunhofer Institute for Production Systems and Design Technology (IPK), Berlin, Germany

    Kevin Haninger

Authors
  1. Kevin Haninger
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kevin Haninger .

Editor information

Editors and Affiliations

  1. Centro de Filosofia da Universidade de Lisboa, University of Lisbon, Lisbon, Portugal

    Maria Isabel Aldinhas Ferreira

  2. Industrial Psychology and Human Factors Group, Cranfield University, Cranfield, UK

    Sarah R. Fletcher

Rights and permissions

Reprints and permissions

Copyright information

© 2022 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Haninger, K. (2022). Robot Inference of Human States: Performance and Transparency in Physical Collaboration. In: Aldinhas Ferreira, M.I., Fletcher, S.R. (eds) The 21st Century Industrial Robot: When Tools Become Collaborators. Intelligent Systems, Control and Automation: Science and Engineering, vol 81. Springer, Cham. https://doi.org/10.1007/978-3-030-78513-0_4

Download citation

Publish with us

Policies and ethics

Search

Navigation