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Quantifying the Human Likeness of a Humanoid Robot

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Abstract

In research of human-robot interactions, human likeness (HL) of robots is frequently used as an individual, vague parameter to describe how a robot is perceived by a human. However, such a simplification of HL is often not sufficient given the complexity and multidimensionality of human-robot interaction. Therefore, HL must be seen as a variable influenced by a network of parameter fields. The first goal of this paper is to introduce such a network which systematically characterizes all relevant aspects of HL. The network is subdivided into ten parameter fields, five describing static aspects of appearance and five describing dynamic aspects of behavior. The second goal of this paper is to propose a methodology to quantify the impact of single or multiple parameters out of these fields on perceived HL. Prior to quantification, the minimal perceivable difference, i.e. the threshold of perception, is determined for the parameters of interest in a first experiment. Thereafter, these parameters are modified in whole-number multiple of the threshold of perception to investigate their influence on perceived HL in a second experiment. This methodology was illustrated on the parameters speed and sequencing (onset of joint movements) of the parameter field movement as well as on the parameter sound. Results revealed that the perceived HL is more sensitive to changes in sequencing than to changes in speed. The sound of the motors during the movement also reduced perceived HL. The presented methodology should guide further, systematic explorations of the proposed network of HL parameters in order to determine and optimize acceptance of humanoid robots.

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Acknowledgements

The authors would like to thank all those who participated in the survey; Katharina Vogt for her assistance with the online questionnaire; Frank Bodin and Johannes Hedinger for initiating and consulting the project and Daniel Kiper for his support in the project.

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Authors and Affiliations

  1. Sensory-Motor Systems Lab, ETH Zurich, 8092, Zurich, Switzerland

    Joachim von Zitzewitz, Peter Wolf & Robert Riener

  2. G-Lab, Center for Neuroprosthetics, EPFL Lausanne, 1015, Lausanne, Switzerland

    Joachim von Zitzewitz

  3. Institute of Applied Simulation, Zurich University of Applied Sciences, 8820, Wädenswil, Switzerland

    Patrick M. Boesch

  4. Medical Faculty, University of Zurich, 8091, Zurich, Switzerland

    Robert Riener

Authors
  1. Joachim von Zitzewitz
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  2. Patrick M. Boesch
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  3. Peter Wolf
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Correspondence to Joachim von Zitzewitz.

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First two authors contributed equally to this work.

Appendix: Movement Parameters

Appendix: Movement Parameters

• Basic Movements: Basic Movements are single movements which cannot be further separated. Complex human movements are build by combining basic movements. This assumption has broad support in science, for example in movement sciences [51], or when movement primitives serve as a foundation for imitation learning [52–54].

• Associated Movements: The parameter Associated Movements is used for assessing human movements [48]. Association of movements exploits synergies of joints or physical effects to increase the efficiency of the overall movement.

• Fluency: Fluency is a typical parameter used to assess human movement [47, 48]. It is defined as the smoothness of a movement [47].

• Stiffness: Humans are able to modify the stiffness of a body part by simultaneously contracting the protagonist and the antagonist muscle [55].

• Range of Motion: Range of Motion is about the realism of movement constraints. It is a classic parameter of movement on joint limits and torque limits [49, 55].

• Complexity: Most human movements can be achieved involving different numbers of degrees of freedom (DoF). The complexity of a movement increases with the number of DoFs involved. The upper limit is maximum number of DoFs available for a movement. For example the maximum complexity achievable by a human hand is 27 DoFs [56].

• Spatiotemporal Variability: Spatiotemporal Variability is another typical parameter of human movement [47, 57]. Kakebeeke [47] defined it as the “variation in displacement, speed and rotation.”

• Velocity Profile: Velocity is a predominant quantitative parameter of human movement [48]. The velocity profiles of typical human movements show approximately the shape of a bell [49, 58, 59]. Robot movements, in order to resemble human movements, should show the same velocity profile.

• Physiological Correctness: An illustrative example to explain Physiological Correctness are the unfamiliar movements of people who are not able to use certain joints or muscles. The problem of low Physiological Correctness is also known in computer animation [55].

• Precision: Humans are able reach very high precision in movements when the movement is fine. However, even if the movement is fast or under high load humans are able to carry it out with precision. How capable is the robot in this respect?

• Efficiency: Efficiency is a parameter used in the assessment of human movements [48, 60]. It is defined as the “minimal motor activity for a task” [60].

• Appropriateness: Appropriateness describes whether the observed movement seems appropriate from a human’s perspective or not. To give an example, imagine a man strolling through the city on a sunny day. A high value of Appropriateness would have the man comfortably walking down the avenue.

• Situatedness: Situatedness relates the movement to the situation and the environment. A high value of Situatedness means that the movement is very well adapted to the current situation [48]. A situated movement is an efficient and safe movement to do in the given situation [55].

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von Zitzewitz, J., Boesch, P.M., Wolf, P. et al. Quantifying the Human Likeness of a Humanoid Robot. Int J of Soc Robotics 5, 263–276 (2013). https://doi.org/10.1007/s12369-012-0177-4

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