Human Understanding of Robot Motion: The Role of Velocity and Orientation

  • Frank PapenmeierEmail author
  • Meike Uhrig
  • Alexandra Kirsch


A general problem in human–robot interaction is how to test the quality of single robot behavior, in order to develop robust and human-acceptable skills. The most typical approach are user tests with subjective measures (questionnaires). We propose a new experimental paradigm that combines subjective measures with an objective behavioral measure, namely viewing times of images viewed as self-paced slide show. We applied this paradigm to human-aware robot navigation. With three experiments, we studied the influence of two aspects of robot motion: velocity profiles and the robot’s orientation. A decreasing velocity profile influenced the predictability of the observed motion, and robot orientations diverting from the robot’s motion vector caused reduced perceived autonomy ratings. We conclude that the viewing time paradigm is a promising tool for studying human-aware robot behavior and that the design of human-aware robot navigation needs to consider both the velocity and the orientation of robots.


Human-aware robot navigation Experimental paradigm Acceptance measures Event cognition 


Compliance with Ethical Standards

Conflict of interest

The authors declare that they have no conflict of interest.


  1. 1.
    Baguley T (2012) Calculating and graphing within-subject confidence intervals for ANOVA. Behav Res Methods 44:158–175. CrossRefGoogle Scholar
  2. 2.
    Bartholow BD, Fabiani M, Gratton G, Bettencourt BA (2001) A psychophysiological examination of cognitive processing of and affective responses to social expectancy violations. Psychol Sci 12(3):197–204. CrossRefGoogle Scholar
  3. 3.
    Bartneck C, Kulić D, Croft E, Zoghbi S (2009) Measurement instruments for the anthropomorphism, animacy, likeability, perceived intelligence, and perceived safety of robots. Int J Soc Robot 1(1):71–81. CrossRefGoogle Scholar
  4. 4.
    Bordwell D (1998) Exceptionally exact perceptions: on staging in depth. In: Bordwell D (ed) On the history of film style. Harvard University Press, Cambridge, pp 158–271Google Scholar
  5. 5.
    Bordwell D, Thompson K (2004) Film art: an introduction. McGraw-Hill, New YorkGoogle Scholar
  6. 6.
    Cohn N, Paczynski M (2013) Prediction, events, and the advantage of agents: the processing of semantic roles in visual narrative. Cogn Psychol 67(3):73–97. CrossRefGoogle Scholar
  7. 7.
    Curiel JM, Radvansky GA (2014) Spatial and character situation model updating. J Cogn Psychol 26(2):205–212. CrossRefGoogle Scholar
  8. 8.
    Hard BM, Recchia G, Tversky B (2011) The shape of action. J Exp Psychol Gen 140:586–604. CrossRefGoogle Scholar
  9. 9.
    Hickethier K (2012) Film und Fernsehanalyse. Metzler, WeimarCrossRefGoogle Scholar
  10. 10.
    Howard CJ, Holcombe AO (2010) Unexpected changes in direction of motion attract attention. Atten Percept Psychophys 72(8):2087–2095. CrossRefGoogle Scholar
  11. 11.
    Just MA, Carpenter PA (1980) A theory of reading: from eye fixations to comprehension. Psychol Rev 87(4):329–354. CrossRefGoogle Scholar
  12. 12.
    Kim B, Pineau J (2016) Socially adaptive path planning in human environments using inverse reinforcement learning. Int J Soc Robot 8(1):51–66. CrossRefGoogle Scholar
  13. 13.
    Kirsch A (2016) Heuristic decision-making for human-aware navigation in domestic environments. In: 2nd global conference on artificial intelligence (GCAI)Google Scholar
  14. 14.
    Kraft RN (1981) The psychological reality of cinematographic principles. Camera angle and cutting. Unpublished dissertation, University of Minnesota, MinneapolisGoogle Scholar
  15. 15.
    Kruse T, Pandey AK, Alami R, Kirsch A (2013) Human-aware robot navigation: a survey. Robot Auton Syst 61(12):1726–1743. CrossRefGoogle Scholar
  16. 16.
    Lemaignan S, Echeverria G, Karg M, Mainprice J, Kirsch A, Alami R (2012) Human–robot interaction in the MORSE simulator. In: Proceedings of the 2012 human–robot interaction conference (late breaking report)Google Scholar
  17. 17.
    Lichtenthäler C, Lorenz T, Kirsch A (2012) Influence of legibility on perceived safety in a virtual human–robot path crossing task. In: RO-MAN, 2012 IEEE, pp 676–681.
  18. 18.
    Lu DV, Smart WD (2013) Towards more efficient navigation for robots and humans. In: IEEE/RSJ international conference on intelligent robots and systems (IROS)Google Scholar
  19. 19.
    Magliano JP, Kopp K, Higgs K, Rapp DN (2016) Filling in the gaps: memory implications for inferring missing content in graphic narratives. Discourse Process. CrossRefGoogle Scholar
  20. 20.
    Magliano JP, Larson AM, Higgs K, Loschky LC (2016) The relative roles of visuospatial and linguistic working memory systems in generating inferences during visual narrative comprehension. Mem Cogn 44:207–219. CrossRefGoogle Scholar
  21. 21.
    Meyerhoff HS, Papenmeier F, Huff M (2013) Object-based integration of motion information during attentive tracking. Perception 42:119–121. CrossRefGoogle Scholar
  22. 22.
    Meyerhoff HS, Papenmeier F, Jahn G, Huff M (2016) Not FLEXible enough: exploring the temporal dynamics of attentional reallocations with the multiple object tracking paradigm. J Exp Psychol Hum Percept Perform 42(6):776–787. CrossRefGoogle Scholar
  23. 23.
    Papenmeier F, Boss A, Mahlke A-K (2018) Action goal changes caused by agents and patients both induce global updating of event models. J Exp Psychol: Learn Mem Cogn.
  24. 24.
    Ray RB (1985) A certain tendency of the Hollywood cinema, 1930–1980. Princeton University Press, PrincetonGoogle Scholar
  25. 25.
    Saerbeck M, Bartneck C (2010) Perception of affect elicited by robot motion. In: Proceedings of the 5th ACM/IEEE international conference on Human–robot interaction, HRI ’10. IEEE Press, Piscataway, pp 53–60.
  26. 26.
    St.Clair R, Huff M, Seiffert AE (2010) Conflicting motion information impairs multiple object tracking. J Vis 10(4:18):1–13. CrossRefGoogle Scholar
  27. 27.
    Thompson K (1985) The continuity system. In: Bordwell D, Staiger J, Thompson K (eds) The classical Hollywood cinema. Film style and mode of production to 1960. Routledge, LondonGoogle Scholar
  28. 28.
    Woods S, Walters M, Koay K, Dautenhahn K (2006) Comparing human robot interaction scenarios using live and video based methods: towards a novel methodological approach. In: 9th IEEE international workshop on advanced motion controlGoogle Scholar
  29. 29.
    Zacks JM, Speer NK, Swallow KM, Braver TS, Reynolds JR (2007) Event perception: a mind-brain perspective. Psychol Bull 133(2):273–293. CrossRefGoogle Scholar
  30. 30.
    Zwaan RA, Langston MC, Graesser AC (1995) The construction of situation models in narrative comprehension: an event-indexing model. Psychol Sci 6(5):292–297. CrossRefGoogle Scholar
  31. 31.
    Zwaan RA, Magliano JP, Graesser AC (1995) Dimensions of situation model construction in narrative comprehension. J Exp Psychol Learn Mem Cogn 21:386–397. CrossRefGoogle Scholar

Copyright information

© Springer Nature B.V. 2018

Authors and Affiliations

  1. 1.Department of PsychologyEberhard Karls Universität TübingenTübingenGermany
  2. 2.Institute of Media StudiesEberhard Karls Universität TübingenTübingenGermany
  3. 3.StuttgartGermany

Personalised recommendations