Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
International Journal of Social Robotics
Published in: International Journal of Social Robotics 5/2019

01-10-2019

Imitation of Human Motion by Low Degree-of-Freedom Simulated Robots and Human Preference for Mappings Driven by Spinal, Arm, and Leg Activity

Authors: Roshni Kaushik, Amy LaViers

Published in: International Journal of Social Robotics | Issue 5/2019

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Robots cannot exactly replicate human motion, especially for low degree-of-freedom (DOF) robots, but perceptual imitation has been accomplished. Nevertheless, the multiple mappings between human and robot bodies continue to present questions around which aspect of human motion robots should preserve. In this vein, this paper presents a methodology for mapping human motion capture data to the motion of a low-DOF simulated robot; further, empirical experiments conducted on Amazon Mechanical Turk illuminate human preference across several such mappings. Users preferred motion capture driven robot motion over artificially generated robot motion, suggesting imitation was successfully accomplished by the proposed mappings. Moreover, one mapping, relating to the leaning of the spine, was preferred over arm- and leg-based mappings by a significant subgroup of respondents who were both loyal to the mapping—across multiple stimuli—and more engaged in the survey than other respondents. These results re-confirm the ability for simple robots to imitate human behavior and indicate that monitoring human spinal activity may be especially useful in this pursuit. Parallel work in psychology and human behavior analysis suggests that successful imitation of the motion of human counterparts is a necessary activity for robots to integrate in human-facing environments.
previous article Robot-Based Motor Rehabilitation in Autism: A Systematic Review
next article A Hybrid Joint/Cartesian DMP-Based Approach for Obstacle Avoidance of Anthropomorphic Assistive Robots

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now
Appendix
Available only for authorised users
Literature
1.
Tomasello M, Kruger AC, Ratner HH (1993) Cultural learning. Behav Brain Sci 16(3):495–511CrossRef
2.
Breazeal C, Scassellati B (2002) Robots that imitate humans. Trends Cognit Sci 6(11):481–487CrossRef
3.
Fong T, Nourbakhsh I, Dautenhahn K (2003) A survey of socially interactive robots. Robot Auton Syst 42(3–4):143–166CrossRef
4.
Kaushik R, Vidrin I, LaViers A (2018) Quantifying coordination in human dyads via a measure of verticality. In: Proceedings of the 5th international conference on movement and computing - MOCO ’18. ACM Press, Genoa, Italy, pp 1–8. https://​doi.​org/​10.​1145/​3212721.​3212805
5.
6.
Boker SM, Rotondo JL (2002) Symmetry building and symmetry breaking in synchronized movement. Mirror Neurons Evol Brain Language 42:163CrossRef
7.
Boker SM, Cohn JF, Theobald BJ, Matthews I, Brick TR, Spies JR (2009) Effects of damping head movement and facial expression in dyadic conversation using real-time facial expression tracking and synthesized avatars. Philos Trans Royal Soc B Biol Sci 364(1535):3485–3495CrossRef
8.
Ashenfelter KT, Boker SM, Waddell JR, Vitanov N (2009) Spatiotemporal symmetry and multifractal structure of head movements during dyadic conversation. J Exp Psychol Hum Percept Perform 35(4):1072CrossRef
9.
Liu C, Ishi CT, Ishiguro H, Hagita N (2012) Generation of nodding, head tilting and eye gazing for human–robot dialogue interaction. In: 2012 7th ACM/IEEE international conference on human–robot interaction (HRI), IEEE, pp 285–292
10.
Johnson DO, Cuijpers RH (2019) Investigating the effect of a humanoid robot’s head position on imitating human emotions. Int J Soc Robot 11(1):65–74CrossRef
11.
Mielke EA, Townsend EC, Killpack MD (2017) Analysis of rigid extended object co-manipulation by human dyads: lateral movement characterization. arXiv preprint arXiv:​1702.​00733
12.
13.
Woolford K (2014) Capturing human movement in the wild. In: Proceedings of the 2014 international workshop on movement and computing, ACM, p 19
14.
Arvind D, Valtazanos A (2009) Speckled tango dancers: Real-time motion capture of two-body interactions using on-body wireless sensor networks. In: Sixth international workshop on wearable and implantable body sensor networks, IEEE, 2009. BSN 2009. pp 312–317
15.
Brown C, Paine G (2015) Interactive tango milonga: Designing internal experience. In: Proceedings of the 2nd international workshop on movement and computing, ACM, pp 17–20
16.
Li W, Pasquier P (2016) Automatic affect classification of human motion capture sequences in the Valence–Arousal model. In: Proceedings of the 3rd international symposium on movement and computing, ACM, p 15
17.
Shiratori T, Nakazawa A, Ikeuchi K (2006) Synthesizing dance performance using musical and motion features. In: Proceedings 2006 IEEE international conference on robotics and automation, 2006. ICRA 2006, IEEE, pp 3654–3659
18.
Kingston P, Egerstedt M (2011) Motion preference learning. In: American control conference (ACC), 2011, IEEE, pp 3819–3824
19.
Pomplun M, Mataric MJ (2000) Evaluation metrics and results of human arm movement imitation. In: Proceedings, first IEEE-RAS international conference on humanoid robotics (Humanoids-2000), pp 7–8
20.
Johnson DO, Cuijpers RH, Pollmann K, van de Ven AA (2016) Exploring the entertainment value of playing games with a humanoid robot. Int J Soc Robot 8(2):247–269CrossRef
21.
Torta E, van Heumen J, Piunti F, Romeo L, Cuijpers R (2015) Evaluation of unimodal and multimodal communication cues for attracting attention in human–robot interaction. Int J Soc Robot 7(1):89–96CrossRef
22.
Noy L, Dekel E, Alon U (2011) The mirror game as a paradigm for studying the dynamics of two people improvising motion together. Proc Natl Acad Sci 108(52):20947–20952CrossRef
23.
Slowinski P, Rooke E, Di Bernardo M, Tanaseva-Atanasova K (2014) Kinematic characteristics of motion in the mirror game. In: 2014 IEEE international conference on systems, man, and cybernetics (SMC), IEEE, pp 748–753
24.
Maeda G, Ewerton M, Lioutikov R, Amor HB, Peters J, Neumann G (2014) Learning interaction for collaborative tasks with probabilistic movement primitives. In: 2014 14th IEEE-RAS international conference on humanoid robots (humanoids), IEEE, pp 527–534
25.
Ewerton M, Neumann G, Lioutikov R, Amor HB, Peters J, Maeda G (2015) Learning multiple collaborative tasks with a mixture of interaction primitives. In: 2015 IEEE international conference on robotics and automation (ICRA), IEEE, pp 1535–1542
26.
Cuykendall S, Schiphorst T, Bizzocchi J (2014) Designing interaction categories for kinesthetic empathy: a case study of synchronous objects. In: Proceedings of the 2014 international workshop on movement and computing, ACM, p 13
27.
Özcimder K, Dey B, Lazier RJ, Trueman D, Leonard NE (2016) Investigating group behavior in dance: an evolutionary dynamics approach. In: American control conference (ACC), 2016, IEEE, pp 6465–6470
28.
Yamane K, Ariki Y, Hodgins J (2010) Animating non-humanoid characters with human motion data. In: Proceedings of the 2010 ACM siggraph/eurographics symposium on computer animation, eurographics association, pp 169–178
29.
Abdul-Massih M, Yoo I, Benes B (2017) Motion style retargeting to characters with different morphologies. Comput Gr Forum Wiley Online Libr 36:86–99CrossRef
30.
Seol Y, O’Sullivan C, Lee J (2013) Creature features: online motion puppetry for non-human characters. In: Proceedings of the 12th ACM siggraph/eurographics symposium on computer animation, ACM, pp 213–221
31.
Bevacqua E, Richard R, Soler J, De Loor P (2016) INGREDIBLE: a platform for full body interaction between human and virtual agent that improves co-presence. In: Proceedings of the 3rd international symposium on movement and computing, ACM, p 22
32.
Tang JK, Chan JC, Leung H (2011) Interactive dancing game with real-time recognition of continuous dance moves from 3D human motion capture. In: Proceedings of the 5th international conference on ubiquitous information management and communication, ACM, p 50
33.
Zhai C, Alderisio F, Słowiński P, Tsaneva-Atanasova K, di Bernardo M (2016) Design of a virtual player for joint improvisation with humans in the mirror game. PloS One 11(4):e0154361CrossRef
34.
Zhai C, Alderisio F, Slowinski P, Tsaneva-Atanasova K, di Bernardo M (2015) Modeling joint improvisation between human and virtual players in the mirror game. arXiv preprint arXiv:​1512.​05619
35.
McCormick J, Vincs K, Nahavandi S, Creighton D, Hutchison S (2014) Teaching a digital performing agent: artificial neural network and hidden markov model for recognising and performing dance movement. In: Proceedings of the 2014 international workshop on movement and computing, ACM, p 70
36.
Gillies M, Brenton H, Kleinsmith A (2015) Embodied design of full bodied interaction with virtual humans. In: Proceedings of the 2nd international workshop on movement and computing, ACM, pp 1–8
37.
Yamane K (2016) Human motion tracking by robots. In: Dance notations and robot motion, Springer, pp 417–430
38.
Ott C, Lee D, Nakamura Y (2008) Motion capture based human motion recognition and imitation by direct marker control. In: 8th IEEE-RAS international conference on humanoid robots, 2008. Humanoids 2008. IEEE, pp 399–405
39.
Minato T, Ishiguro H (2007) Generating natural posture in an android by mapping human posture in three-dimensional position space. In: IEEE/RSJ international conference on intelligent robots and systems, 2007. IROS 2007. IEEE, pp 609–616
40.
Fujimoto I, Matsumoto T, De Silva PRS, Kobayashi M, Higashi M (2011) Mimicking and evaluating human motion to improve the imitation skill of children with autism through a robot. Int J Soc Robot 3(4):349–357CrossRef
41.
Nakaoka S, Nakazawa A, Yokoi K, Hirukawa H, Ikeuchi K (2003) Generating whole body motions for a biped humanoid robot from captured human dances. In: 2003 IEEE international conference on robotics and automation (Cat. No. 03CH37422), IEEE, vol 3, pp 3905–3910
42.
Demiris Y, Johnson M (2003) Distributed, predictive perception of actions: a biologically inspired robotics architecture for imitation and learning. Connect Sci 15(4):231–243CrossRef
43.
Billard A, Matarić MJ (2001) Learning human arm movements by imitation: evaluation of a biologically inspired connectionist architecture. Robot Auton Syst 37(2–3):145–160CrossRef
44.
Suleiman W, Yoshida E, Kanehiro F, Laumond JP, Monin A (2008) On human motion imitation by humanoid robot. In: 2008 IEEE international conference on robotics and automation, IEEE, pp 2697–2704
45.
Nehaniv C, Dautenhahn K (1998) Mapping between dissimilar bodies: a ordances and the algebraic foundations of imitation. EWLR-98, pp 64–72
46.
Alissandrakis A, Nehaniv CL, Dautenhahn K, Saunders J (2006) Evaluation of robot imitation attempts: comparison of the system’s and the human’s perspectives. In: Proceeding of the 1st ACM sigchi/sigart conference on human–robot interaction - HRI ’06, ACM Press, Salt Lake City, p 134. https://​doi.​org/​10.​1145/​1121241.​1121265
47.
48.
Simmons R, Knight H (2017) Keep on dancing: effects of expressive motion mimicry. In: 2017 26th IEEE international symposium on robot and human interactive communication (RO-MAN), IEEE, pp 720–727
49.
Knight H, Simmons R (2016) Laban head-motions convey robot state: a call for robot body language. In: 2016 IEEE international conference on robotics and automation (ICRA), IEEE, pp 2881–2888
50.
Knight H (2016) Expressive motion for low degree-of-freedom robots
51.
Sharma M, Hildebrandt D, Newman G, Young JE, Eskicioglu R (2013) Communicating affect via flight path: exploring use of the laban effort system for designing affective locomotion paths. In: Proceedings of the 8th ACM/IEEE international conference on human-robot interaction, IEEE Press, pp 293–300
52.
Dragan AD, Ratliff ND, Srinivasa SS (2011) Manipulation planning with goal sets using constrained trajectory optimization. In: 2011 IEEE international conference on robotics and automation, IEEE, pp 4582–4588
53.
Chan MT, Gorbet R, Beesley P, Kulić D (2016) Interacting with curious agents: user experience with interactive sculptural systems. In: 2016 25th IEEE international symposium on robot and human interactive communication (RO-MAN), IEEE, pp 151–158
54.
Wang H, Kosuge K (2012) Control of a robot dancer for enhancing haptic human-robot interaction in waltz. IEEE Trans Haptics 5(3):264–273CrossRef
55.
Baillieul J, Özcimder K (2012) The control theory of motion-based communication: problems in teaching robots to dance. In: American control conference (ACC), 2012, IEEE, pp 4319–4326
56.
Takeda T, Hirata Y, Kosuge K (2007) Dance partner robot cooperative motion generation with adjustable length of dance step stride based on physical interaction. In: IEEE/RSJ international conference on intelligent robots and systems, 2007. IROS 2007. IEEE, pp 3258–3263
57.
Hölldampf J, Peer A, Buss M (2010) Synthesis of an interactive haptic dancing partner. In: RO-MAN, 2010 IEEE, IEEE, pp 527–532
58.
Johnson DO, Cuijpers RH, Juola JF, Torta E, Simonov M, Frisiello A, Bazzani M, Yan W, Weber C, Wermter S (2014) Socially assistive robots: a comprehensive approach to extending independent living. Int J Soc Robot 6(2):195–211CrossRef
59.
Torta E, Werner F, Johnson DO, Juola JF, Cuijpers RH, Bazzani M, Oberzaucher J, Lemberger J, Lewy H, Bregman J (2014) Evaluation of a small socially-assistive humanoid robot in intelligent homes for the care of the elderly. J Intell Robot Syst 76(1):57–71CrossRef
60.
Van Dijk ET, Torta E, Cuijpers RH (2013) Effects of eye contact and iconic gestures on message retention in human–robot interaction. Int J Soc Robot 5(4):491–501CrossRef
Metadata
Title
Imitation of Human Motion by Low Degree-of-Freedom Simulated Robots and Human Preference for Mappings Driven by Spinal, Arm, and Leg Activity
Authors
Roshni Kaushik
Amy LaViers
Publication date
01-10-2019
Publisher
Springer Netherlands
Published in
International Journal of Social Robotics / Issue 5/2019
Print ISSN: 1875-4791
Electronic ISSN: 1875-4805
DOI
https://doi.org/10.1007/s12369-019-00595-y

Other articles of this Issue 5/2019

International Journal of Social Robotics 5/2019 Go to the issue

OriginalPaper

Culture as a Sensor? A Novel Perspective on Human Activity Recognition

OriginalPaper

Perceived Mistreatment and Emotional Capability Following Aggressive Treatment of Robots and Computers

OriginalPaper

Could Social Robots Make Us Kinder or Crueller to Humans and Animals?

OriginalPaper

Human–Robot Collaboration Acceptance Model: Development and Comparison for Germany, Japan, China and the USA

ReviewPaper

Robot-Based Motor Rehabilitation in Autism: A Systematic Review

OriginalPaper

On-the-Fly Detection of User Engagement Decrease in Spontaneous Human–Robot Interaction Using Recurrent and Deep Neural Networks

Premium Partners

    Image Credits