Evaluating ToRCH Structure for Characterizing Robots

  • Manal LinjawiEmail author
  • Roger K. Moore
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11650)


Robots are increasingly used in different scenarios, depending on the development of their capabilities and performance. The accelerating growth of robotics applications requires a tool that can comprehensively capture a wide range of robot capabilities. In this study, we evaluate robot capabilities using a structure known as “Towards Robot Characterization” (ToRCH) recently developed to meet this need. This structure defines robot capabilities and consequently enables capabilities and applications to be mapped against each other. An experiment was conducted to obtain the capabilities of two scenarios presented by the NAO robot. The method used to capture the capabilities was performed via the ToRCH structure. ToRCH implicitly illustrates the scenarios in a simple capability profile. This research assesses two aspects of the ToRCH capabilities capturing process. First, it verifies the moderate agreement level among roboticists in using ToRCH to capture the robot’s capabilities. Second, it demonstrates the richness of the ToRCH structure for capturing robot capabilities compared to the Multi-Annual Roadmap (MAR) levels. This initial study evaluates the ToRCH method in extracting different capability levels and illustrating them in a robot capability profile. It therefore highlights the potential of ToRCH in classifying robots.


Robot capabilities Capabilities profile Robot characterization 


  1. 1.
    Anzalone, S.M., Boucenna, S., Ivaldi, S., Chetouani, M.: Evaluating the engagement with social robots. Int. J. Soc. Robot. 7(4), 465–478 (2015)CrossRefGoogle Scholar
  2. 2.
    Bhatnagar, S., et al.: Mapping intelligence: requirements and possibilities. In: Müller, V.C. (ed.) PT-AI 2017. SAPERE, vol. 44, pp. 117–135. Springer, Cham (2018). Scholar
  3. 3.
    Bonsignorio, F., Del Pobil, A.P., Messina, E.: Fostering progress in performance evaluation and benchmarking of robotic and automation systems [tc spotlight]. IEEE Robot. Autom. Mag. 21(1), 22–25 (2014)CrossRefGoogle Scholar
  4. 4.
    Dobra, A.: General classification of robots. size criteria. In: Proceedings of 23rd International Conference on Robotics in Alpe-Adria-Danube Region (RAAD), pp. 1–6. IEEE (2014)Google Scholar
  5. 5.
    Gouaillier, D., et al.: Mechatronic design of NAO humanoid. In: 2009 IEEE International Conference on Robotics and Automation, pp. 769–774, May 2009Google Scholar
  6. 6.
    Landis, J.R., Koch, G.G.: The measurement of observer agreement for categorical data. Biometrics 33, 159–174 (1977)CrossRefGoogle Scholar
  7. 7.
    Lee, J., Kim, D., Ryoo, H.Y., Shin, B.S.: Sustainable wearables: wearable technology for enhancing the quality of human life. Sustainability 8(5), 466 (2016)CrossRefGoogle Scholar
  8. 8.
    Lenzi, T., et al.: Measuring human-robot interaction on wearable robots: a distributed approach. Mechatronics 21(6), 1123–1131 (2011)CrossRefGoogle Scholar
  9. 9.
    Linjawi, M., Moore, R.K.: Towards a comprehensive taxonomy for characterizing robots. In: Giuliani, M., Assaf, T., Giannaccini, M.E. (eds.) TAROS 2018. LNCS (LNAI), vol. 10965, pp. 381–392. Springer, Cham (2018). Scholar
  10. 10.
    Rastic-Dulborough, O.: Internet of things (IoT) and human computer interaction. Technical report, University of Southampton (2014)Google Scholar
  11. 11.
    Interrater reliability. J. Consum. Psychol. 10(1&2), 71–73 (2001)Google Scholar
  12. 12.
    Siciliano, B., Khatib, O.: Springer Handbook of Robotics. Springer, Heidelberg (2016).
  13. 13.
    SPARC Robotics, eu-Robotics AISBL, Brussels, Belgium: Robotics Multi-Annual Roadmap for Robotics in Europe, Horizon 2020 (2016)Google Scholar
  14. 14.
    Stead, L., Goulev, P., Evans, C., Mamdani, E.: The emotional wardrobe. Pers. Ubiquit. Comput. 8(3–4), 282–290 (2004)Google Scholar
  15. 15.
    Winfield, A.: Robotics: A Very Short Introduction. Oxford University Press, Oxford (2012)CrossRefGoogle Scholar
  16. 16.
    Winfield, A.F.T.: How intelligent is your intelligent robot? (2017). arxiv:1712.08878
  17. 17.
    Yousef, H., Boukallel, M., Althoefer, K.: Tactile sensing for dexterous in-hand manipulation in robotics-a review. Sens. Actuators, A 167(2), 171–187 (2011)CrossRefGoogle Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of SheffieldSheffieldUK

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