Advertisement

Shared Control for Human-Robot Cooperative Manipulation Tasks

Conference paper
Part of the Mechanisms and Machine Science book series (Mechan. Machine Science, volume 49)

Abstract

In the past decade many studies on human motor control have investigated how humans are moving their arms. In robotics, these studies were usually used as a foundation for human-robot cooperation tasks. Nonetheless, the gap between human motor control and robot control remains challenging. In this paper we investigated, how human proprioceptive abilities could enhance performance of cooperative manipulative tasks, where humans and robots are autonomous agents coupled through physical interaction. In such setups, the robot movements are usually accurate but without the proprioceptive capabilities observed in humans. On the contrary, humans have well developed proprioceptive capabilities, but their movement accuracy is highly dependent on the speed of movement. In this paper we proposed an approach where we exploited the speed-accuracy trade-off model of a human together with the robotic partner. In this way the performance can be improved in a human-robot cooperative setup. The performance was analyzed on a task where a long object, i.e. a pipe, needs to be manipulated into a groove with different tolerances. We tested the accuracy and efficiency of performing the task. The results show that the proposed approach can successfully estimate human behavior and successfully perform the task.

Keywords

Human-robot cooperation Robot adaptation 

Notes

Acknowledgment

The work presented in this paper was supported by the European Unions Horizon 2020 research and innovation programme under grant agreement No 687662 - SPEXOR.

References

  1. 1.
    Ajoudani A, Tsagarakis NG, Bicchi A (2012) Tele-impedance: teleoperation with impedance regulation using a body-machine interface. Int J Robot Res 31(13):1642–1656.http://ijr.sagepub.com/cgi/content/long/31/13/1642ijr.sagepub.com/cgi/doi/10.1177/0278364912464668
  2. 2.
    Alimi AM (1997) Speed/accuracy trade-offs in target-directed movements. Behav Brain Sci 20:279–349Google Scholar
  3. 3.
    Argall BD, Billard AG (2010) A survey of tactile human-robot interactions. Robot Auton Syst 58(10):1159–1176. http://dx.doi.org/10.1016/j.robot.2010.07.002linkinghub.elsevier.com/retrieve/pii/S0921889010001375
  4. 4.
    Ben Amor H, Berger E, Vogt D, Jung B (2009) Kinesthetic bootstrapping: teaching motor skills to humanoid robots through physical interaction. Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics) (LNAI), vol 5803, pp 492–499Google Scholar
  5. 5.
    Berardelli A, Hallett M, Rothwell JC, Agostino R, Manfredi M, Thompson PD, Marsden CD (1996) Single-joint rapid arm movements in normal subjects and in patients with motor disorders. Brain J Neurol 119(Pt 2(2)):661–674. http://brain.oxfordjournals.org/cgi/doi/10.1093/brain/119.2.661
  6. 6.
    Bootsma RJ, Fernandez L, Mottet D (2004) Behind Fitts’ law: kinematic patterns in goal-directed movements. Int J Hum Comput Stud 61(6):811–821CrossRefGoogle Scholar
  7. 7.
    Calinon S, Billard AG (2007) What is the teacher’s role in robot programming bydemonstration?: toward benchmarks for improved learning. Interact Stud 8(3), 441–464. http://www.ingentaconnect.com/content/jbp/is/2007/00000008/00000003/art00006, https://benjamins.com/catalog/is.8.3.08cal
  8. 8.
    Cos I, Belanger N, Cisek P (2011) The influence of predicted arm biomechanics on decision making. J Neurophysiol 105(6):3022–3033. http://www.ncbi.nlm.nih.gov/pubmed/21451055 jn.physiology.org/cgi/doi/10.1152/jn.00975.2010
  9. 9.
    Denisa M, Gams A, Ude A, Petric T (2016) Learning compliant movement primitives through demonstration and statistical generalization. IEEE/ASME Trans Mechatron 21(5):2581–2594. http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7360201 ieeexplore.ieee.org/document/7360201/
  10. 10.
    Fitts PM (1954) The information capacity of the human motor. J Exp Biol 47(6):381–391Google Scholar
  11. 11.
    Franklin DW, Wolpert DM (2011) Computational mechanisms of sensorimotor control. http://dx.doi.org/10.1016/j.neuron.2011.10.006
  12. 12.
    Gomi H, Kawato M (1993) Neural network control for a closed-loop system using feedback-error-learning. http://linkinghub.elsevier.com/retrieve/pii/S089360800980004X
  13. 13.
    Hollerbach JM, Atkeson CG (1987) Deducing planning variables from experimental arm trajectories: pitfalls and possibilities. Biol Cybern 56(5–6):279–292. http://link.springer.com/10.1007/BF01068748link.springer.com/10.1007/BF00319509
  14. 14.
    Ijspeert AJ, Nakanishi J, Hoffmann H, Pastor P, Schaal S (2013) Dynamical movement primitives: learning attractor models for motor behaviors. Neural Comput 25:328–373. http://www.ncbi.nlm.nih.gov/pubmed/23148415
  15. 15.
    Ikemoto S, Amor H, Minato T, Jung B, Ishiguro H (2012) Physical human-robot interaction: mutual learning and adaptation. IEEE Robot Autom Mag 19(4):24–35. http://www.ias.informatik.tu-darmstadt.de/uploads/Publications/phriAmor.pdf ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6161710
  16. 16.
    Khatib O, Yokoi K, Brock O, Chang K, Casal A (1999) Robots in human environments: basic autonomous capabilities. Int J Robot Res 18(7), 684–696. http://ijr.sagepub.com/content/18/7/684.abstract
  17. 17.
    Petrič T, Goljat R, Babič J: Cooperative human-robot control based on Fitts’ law. In: 2016 IEEE-RAS international conference on humanoid robots (2016)Google Scholar
  18. 18.
    Sabes PN, Jordan MI (1997) Obstacle avoidance and a perturbation sensitivity model for motor planning. J Neurosci Off J Soc Neurosci 17(18), 7119–7128Google Scholar
  19. 19.
    Vanderborght B, Albu-Schaeffer A, Bicchi A, Burdet E, Caldwell DG, Carloni R, Catalano M, Eiberger O, Friedl W, Ganesh G, Garabini M, Grebenstein M, Grioli G, Haddadin S, Hoppner H, Jafari A, Laffranchi M, Lefeber D, Petit F, Stramigioli S, Tsagarakis N, Van Damme M, Van Ham R, Visser LC, Wolf S (2013) Variable impedance actuators: a review. Robot Auton Syst 61(12):1601–1614. http://dx.doi.org/10.1016/j.robot.2013.06.009
  20. 20.
    Yohanan S, MacLean KE (2012) The role of affective touch in human-robot interaction: human intent and expectations in touching the haptic creature. Int J Soc Robot 4(2):163–180. http://link.springer.com/10.1007/s12369-011-0126-7
  21. 21.
    Zhai S, Kong J, Ren X (2004) Speedaccuracy tradeoff in Fitts’ law taskson the equivalency of actual and nominal pointing precision. Int J Hum Comput Stud 61(6):823–856. http://linkinghub.elsevier.com/retrieve/pii/S1071581904001028

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Department for Automation, Biocybernetics and RoboticsJožef Stefan Institute (JSI)LjubljanaSlovenia

Personalised recommendations