Introduction to the Evolution of Physical Systems Special Issue

Abstract

We are delighted to introduce this Special Issue on the Evolution of Physical Systems, the culmination of a series of workshops organized around the topic that began with ALIFE XIII (2012) and ran through ECAL 2013, ALIFE XIV (2014), and ECAL 2015.Inspired by our mutual interests, we coined the term evolution of physical systems (EPS) to describe evolutionary approaches that occur in real-world physical substrates rather than in simulation. We deliberately chose this term to be broad enough to encompass both parallel embodied evolution [19], in which evolution is distributed across a population of robots, and more classical evolutionary robotics work, where evaluation is serialized on a single robot, as by Floreano and Mondada [5].The evolution of physical systems has its roots in the embodiment philosophy of Rodney Brooks, who famously said “the world is its own best model” [3]. Brooks' emphatic critique of the symbol system hypothesis argued that relatively simple systems, developed and grounded in suitably complex real-world environments, can lead to the emergence of complex behaviors. Contemporary approaches to the EPS aim at automating Brooks' vision by exploiting evolutionary algorithms on systems that “live” and “evolve” in the real world, that is, whose behavior (and possibly their form) are as grounded as possible in the environment.Embodied evolutionary algorithms first gained prominence with the “Sussex approach” of Harvey et al. [7] in the 1990s. The Sussex gantry-based robot [4, 6], driven by a neural network, was capable of robust behaviors in a noisy real-world environment. Their motivation for using the real-world rather than a simulated environment stemmed largely from the fact that in that era of limited computational power, it was faster and more efficient to test networks in situ than it was to develop and run a simulator that realistically modeled sensor noise. This notion, that it is sometimes more practical (and computationally efficient) to dispense with simulation entirely and evolve behaviors directly in the real world, persists at the heart of the EPS.Two other results of the Sussex group are particularly noteworthy. First is Adrian Thompson's silicon evolution of a field-programmable gate array (FPGA) [17, 18]. The chosen task was to evolve a circuit capable of discriminating between 1- and 10-kHz signals without the use of a clock. By evolving in silicon, rather than in simulation, Thompson was able to generate nearly inscrutable solutions that exploited the analogue (and non-simulable) nature of FPGAs. This idea of using embodiment to arrive at “novel surprise” solutions is a further guiding principle of the EPS. The second is Jakobi's essential work on the reality gap [8], which highlighted the difficulty in transferring results evolved in simulation into the real world. Jakobi notes that the best results emerge when “the noise levels of the simulation have similar amplitudes to those observed in reality,” and points out that these approaches become less feasible as environments and sensors become more complex.The practice of evolving outside of simulators grew from there. Floreano and Mondada [5] were able to evolve robust wall-following behavior in a tethered Khepera robot through embodiment. Watson et al. [19] took embodiment a step further by simultaneously embedding an entire population of evolving TupperBots into a shared arena—and demonstrated how the behaviors that emerged through this process were qualitatively different, and more effective, than those evolved in a simulated environment (their use of an electrified floor as an alternative to tethers was particularly innovative). Zykov et al. evolved dynamic open-loop gaits on a large hexapod robot, remarking upon the amount of labor required to reset the large robot between physical trials [21].Several of our own research areas have been in this realm of problems that are easier to test physically than they are to simulate realistically.