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Robotic Simulations, Simulations of Robots

Abstract

Simulation studies have been carried out in robotics for a variety of epistemic and practical purposes. Here it is argued that two broad classes of simulation studies can be identified in robotics research. The first one is exemplified by the use of robotic systems to acquire knowledge on living systems in so-called biorobotics, while the second class of studies is more distinctively connected to cases in which artificial systems are used to acquire knowledge about the behaviour of autonomous mobile robots. The two classes pertain to sub-areas of robotics which are apparently quite distant from one another in terms of goals, methodologies, technologies, and theoretical backgrounds. Still both are concerned with building, running, and experimenting on simulations of other systems. This paper aims to reveal and discuss some methodological commonalities between the two classes of studies. Philosophical literature on simulation methodologies has been traditionally focused on studies carried out in research fields other than robotics: this article may therefore contribute to shedding light on how the concept of simulation is used in robotics, and on the role simulation methodologies play in this research field.

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Fig. 1

Notes

  1. 1.

    There is a limited methodological literature on simulations in biorobotics (Datteri 2017; Datteri and Tamburrini 2007; Webb 2001, 2006), and no methodological literature on the role of simulations in autonomous mobile robotics.

  2. 2.

    The question whether the hardware or the software of a computer is what simulates the target system (or a model of it) in computer simulation studies has been debated in the philosophical literature (Barberousse et al. 2009; Beisbart 2018). Here the simulation system is taken to be a programmed computer described symbolically, i.e., in terms of variables taking values and of relationships holding among them. However, different interpretations of the notion of “programmed computer” may be compatible with the analysis made here.

  3. 3.

    This construal of the concept of “theoretical model” is substantially akin to the analysis of models as set-theoretic structures (Suppe 1989).

  4. 4.

    It will not be assumed that all theoretical models are explanatory. The problem of what makes a theoretical model explanatory is out of the scope of this article: for an up-to-date discussion, see Bokulich (2017).

  5. 5.

    The model-oriented strategy has been sometimes called “synthetic method” in artificial intelligence and cybernetics (Cordeschi 2002). Note that this strategy can only lead one to reduce or increase the space of the how-possibly theoretical models of S’ behaviour. A’s reproduction of the latter, per se, guarantees neither that M is the only possible model of it nor that it is explanatory.

  6. 6.

    An interesting difference between these two simulation case-studies is worth emphasizing here. In the RFID study the simulated robot is based on the real-life robot Zerg and reproduces the same physical properties of the real-life one. The robots simulated in the swarm study, on the contrary, are purely fictional and not modelled after any real-life robots. Still, both studies have predictive purposes. The purpose of the swarm study, in particular, is to predict the behavior of purely fictional entities which reproduce no existing robot.

References

  1. Ambros-Ingerson, J., Granger, R., & Lynch, G. (1990). Simulation of paleocortex performs hierarchical clustering. Science, 247(4948), 1344–1348.

    Article  Google Scholar 

  2. Arkin, R. C. (1998). Behavior-based robotics. Cambridge: The MIT Press.

    Google Scholar 

  3. Balakirsky, S., Carpin, S., Kleiner, A., Lewis, M., Visser, A., Wang, J., et al. (2007). Towards heterogeneous robot teams for disaster mitigation: Results and performance metrics from Robocup rescue. Journal of Field Robotics, 24(11), 943–967.

    Article  Google Scholar 

  4. Barberousse, A., Franceschelli, S., & Imbert, C. (2009). Computer simulations as experiments. Synthese, 169(3), 557–574.

    MathSciNet  Article  Google Scholar 

  5. Beisbart, C. (2018). Are computer simulations experiments? And if not, how are they related to each other? European Journal for Philosophy of Science, 8(2), 171–204.

    MathSciNet  Article  Google Scholar 

  6. Blanchard, M., Rind, F. C., & Verschure, P. F. M. J. (2000). Collision avoidance using a model of the locust LGMD neuron. Robotics and Autonomous Systems, 30(1–2), 17–38.

    Article  Google Scholar 

  7. Bokulich, A. (2017). Models and Explanation. In L. Magnani & T. Bertolotti (Eds.), Springer handbook of model-based science (pp. 103–118). New York and Cham: Springer.

    Chapter  Google Scholar 

  8. Braitenberg, V. (1986). Vehicles. Experiments in synthetic psychology. Cambridge: The MIT Press.

    Google Scholar 

  9. Cheah, C. C., Hou, S. P., & Slotine, J. J. (2009). Region-based shape control for a swarm of robots. Automatica, 45(10), 2406–2411.

    MathSciNet  Article  MATH  Google Scholar 

  10. Cordeschi, R. (2002). The discovery of the artificial. Behavior, mind and machines before and beyond cybernetics. Dordrecht: Springer.

    Google Scholar 

  11. Craver, C. F. (2006). When mechanistic models explain. Synthese, 153(3), 355–376.

    MathSciNet  Article  Google Scholar 

  12. Datteri, E. (2017). The epistemic value of brain–machine systems for the study of the brain. Minds and Machines, 27(2), 287–313.

    Article  Google Scholar 

  13. Datteri, E., & Tamburrini, G. (2007). Biorobotic experiments for the discovery of biological mechanisms. Philosophy of Science, 74(3), 409–430.

    Article  Google Scholar 

  14. Dror, R. O., Dirks, R. M., Grossman, J. P., Xu, H., & Shaw, D. E. (2012). Biomolecular simulation: A computational microscope for molecular biology. Annual Review of Biophysics, 41(1), 429–452.

    Article  Google Scholar 

  15. Feigenbaum, E. (1961). The simulation of verbal learning behavior. In Papers presented at the May 9–11, 1961, Western joint IRE-AIEE-ACM computer conference, pp. 121–132.

  16. Frigg, R., & Nguyen, J. (2017). Models and representation. Springer handbook of model-based science (pp. 49–102). Cham: Springer.

    Chapter  Google Scholar 

  17. Glennan, S. (2017). The new mechanical philosophy. Oxford: Oxford University Press.

    Book  Google Scholar 

  18. Grasso, F. W., Consi, T. R., Mountain, D. C., & Atema, J. (2000). Biomimetic robot lobster performs chemo-orientation in turbulence using a pair of spatially separated sensors: Progress and challenges. Robotics and Autonomous Systems, 30(1–2), 115–131.

    Article  Google Scholar 

  19. Guala, F. (2002). Models, Simulations, and Experiments. In L. Magnani & N. J. Nersessian (Eds.), Model-based reasoning. Science, technology, values (pp. 59–74). New York: Springer US.

  20. Hartmann, S. (1996). The world as a process: simulations in the natural and social sciences. In R. Hegselmann, et al. (Eds.), Simulation and modeling in the social sciences from the philosophy of science point of view (pp. 77–100). Dordrecht: Theory and Decision Library, Kluwer.

    Google Scholar 

  21. Humphreys, P. (2004). Extending ourselves: Computational science, empiricism, and scientific method. New York: Oxford University Press.

    Book  Google Scholar 

  22. Jennings, J. S., Orleans, N., Whelan, G., & Evans, W. F. (1997). Cooperative search and rescue with a team of mobile robots. In Proceedings of the IEEE Int. Conf. on Advanced Robotics (ICAR), (pp. 193–200).

  23. Kleiner, A., Prediger, J., & Nebel, B. (2006). RFID Technology-based exploration and SLAM for search and rescue. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), (pp. 4054–4059).

  24. Long, J. (2012). Darwin’s Devices. What evolving robots can teach us about the history of life and the future of technology. New York: Basic Books.

    Google Scholar 

  25. Long, J. H., Schumacher, J., Livingston, N., & Kemp, M. (2006). Four flippers or two? Tetrapodal swimming with an aquatic robot. Bioinspiration & Biomimetics, 1(1), 20–29.

    Article  Google Scholar 

  26. Pfeifer, R. (2009). Biologically inspired robotics. Science, 1088 (2007).

  27. Rosenblueth, A., & Wiener, N. (1945). The role of models in science. Philosophy of Science, 12(4), 316–321.

    Article  Google Scholar 

  28. Siciliano, B., & Khatib, O. (Eds.). (2008). Springer handbook of robotics. Heidelberg: Springer.

    MATH  Google Scholar 

  29. Simon, H. A., & Newell, A. (1962). Computer simulation of human thinking and problem solving. Monographs of the Society for Research in Child Development, 27(2), 137.

    Article  Google Scholar 

  30. Suppe, F. (1989). The semantic conception of theories and scientific realism. Urbana and Chicago: University of Illinois Press.

    Google Scholar 

  31. Swoyer, C. (1991). Structural representation and surrogative reasoning. Synthese, 87(3), 449–508.

    MathSciNet  Article  Google Scholar 

  32. Tamburrini, G., & Datteri, E. (2005). Machine experiments and theoretical modelling: From cybernetic methodology to neuro-robotics. Minds and Machines, 15(3–4), 335–358.

    Article  Google Scholar 

  33. Webb, B. (2001). Can robots make good models of biological behaviour? The Behavioral and Brain Sciences, 24(6), 1033–1050–1094.

  34. Webb, B. (2006). Validating biorobotic models. Journal of Neural Engineering, 3, R25–R35.

    Article  Google Scholar 

  35. Weisberg, M. (2013). Simulation and similarity. using models to understand the world. Oxford: Oxford University Press.

    Book  Google Scholar 

  36. Winsberg, E. (1999). Sanctioning models: The epistemology of simulation. Science in Context, 12(2), 275–292.

    Article  Google Scholar 

  37. Ziemke, T. (2003). On the role of robot simulations in embodied cognitive science. AISB Journal, 1(4), 389–399.

    Google Scholar 

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Correspondence to Viola Schiaffonati.

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Datteri, E., Schiaffonati, V. Robotic Simulations, Simulations of Robots. Minds & Machines 29, 109–125 (2019). https://doi.org/10.1007/s11023-019-09490-x

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Keywords

  • Robotic simulation
  • Simulation system
  • Biorobotics
  • Autonomous robotics