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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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.
This construal of the concept of “theoretical model” is substantially akin to the analysis of models as set-theoretic structures (Suppe 1989).
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).
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.
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.
Ambros-Ingerson, J., Granger, R., & Lynch, G. (1990). Simulation of paleocortex performs hierarchical clustering. Science, 247(4948), 1344–1348.
Arkin, R. C. (1998). Behavior-based robotics. Cambridge: The MIT Press.
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.
Barberousse, A., Franceschelli, S., & Imbert, C. (2009). Computer simulations as experiments. Synthese, 169(3), 557–574.
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.
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.
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.
Braitenberg, V. (1986). Vehicles. Experiments in synthetic psychology. Cambridge: The MIT Press.
Cheah, C. C., Hou, S. P., & Slotine, J. J. (2009). Region-based shape control for a swarm of robots. Automatica, 45(10), 2406–2411.
Cordeschi, R. (2002). The discovery of the artificial. Behavior, mind and machines before and beyond cybernetics. Dordrecht: Springer.
Craver, C. F. (2006). When mechanistic models explain. Synthese, 153(3), 355–376.
Datteri, E. (2017). The epistemic value of brain–machine systems for the study of the brain. Minds and Machines, 27(2), 287–313.
Datteri, E., & Tamburrini, G. (2007). Biorobotic experiments for the discovery of biological mechanisms. Philosophy of Science, 74(3), 409–430.
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.
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.
Frigg, R., & Nguyen, J. (2017). Models and representation. Springer handbook of model-based science (pp. 49–102). Cham: Springer.
Glennan, S. (2017). The new mechanical philosophy. Oxford: Oxford University Press.
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.
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.
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.
Humphreys, P. (2004). Extending ourselves: Computational science, empiricism, and scientific method. New York: Oxford University Press.
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).
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).
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.
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.
Pfeifer, R. (2009). Biologically inspired robotics. Science, 1088 (2007).
Rosenblueth, A., & Wiener, N. (1945). The role of models in science. Philosophy of Science, 12(4), 316–321.
Siciliano, B., & Khatib, O. (Eds.). (2008). Springer handbook of robotics. Heidelberg: Springer.
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.
Suppe, F. (1989). The semantic conception of theories and scientific realism. Urbana and Chicago: University of Illinois Press.
Swoyer, C. (1991). Structural representation and surrogative reasoning. Synthese, 87(3), 449–508.
Tamburrini, G., & Datteri, E. (2005). Machine experiments and theoretical modelling: From cybernetic methodology to neuro-robotics. Minds and Machines, 15(3–4), 335–358.
Webb, B. (2001). Can robots make good models of biological behaviour? The Behavioral and Brain Sciences, 24(6), 1033–1050–1094.
Webb, B. (2006). Validating biorobotic models. Journal of Neural Engineering, 3, R25–R35.
Weisberg, M. (2013). Simulation and similarity. using models to understand the world. Oxford: Oxford University Press.
Winsberg, E. (1999). Sanctioning models: The epistemology of simulation. Science in Context, 12(2), 275–292.
Ziemke, T. (2003). On the role of robot simulations in embodied cognitive science. AISB Journal, 1(4), 389–399.
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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
- Robotic simulation
- Simulation system
- Autonomous robotics