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Robotic Simulations, Simulations of Robots


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


  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.


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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).

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  • Robotic simulation
  • Simulation system
  • Biorobotics
  • Autonomous robotics