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Therefore, the hypothesis of a difference between the processes that have generated the results needs to be validated with appropriate statistical tests Bartz-Beielstein, Second, evolutionary robotics experiments are, in general, performed on a simplified model of a real robot or animal.

Drawing any conclusion on the real robot or animal requires discussing to what extent the model is appropriate to study the research question Hughes, ; Long, The opportunistic nature of evolutionary algorithms makes this particularly mandatory, as the evolutionary process may have exploited features that are specific to the simplified model, and which may not hold on the targeted system, giving rise to a reality gap Jakobi et al. Although we have emphasized that all experimental research in ER should follow the general template above, differences in objectives can have implications for the methodology.

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For instance, a study aiming at solving a particular engineering problem can be successfully concluded by only inspecting the end result of the evolutionary process, the evolved robot. Verifying success requires the validation of the robot behavior as specified in the problem description — analysis of the evolutionary runs is not relevant for this purpose.

Table of Contents

On the other hand, comparing algorithmic implementations of ER principles requires thorough statistical analysis of these variants. To this end, the existing practice of evolutionary computation can be very helpful. This practice is based on using well-specified test problems, problem instance generators, definitions of algorithm performance, enough repetitions with different random seeds, and the correct use of statistics.

This methodology is known and proven, offering established choices for the most important elements of the experimental workflow. For instance, there are many repositories of test problems, several problem instance generators, and there is broad agreement about the important measures of algorithm performance, cf.

Chapter 13 in Eiben and Smith In the current evolutionary robotics literature, proof-of-concept studies are common; these typically show that a robot controller or morphology can be evolved that induces certain desirable or otherwise interesting behaviors. The choice of targeted behaviors fitness functions and robot environments is to a large extent ad hoc , and the use of standard test suites and benchmarking is not as common as in evolutionary computing.

Whether adopting such practices from EC is actually possible and desirable is an issue that should be discussed in the community. Evolutionary robotics is a relatively young field with a history of about two decades; it is still in development. Robots can be controlled by many different kinds of controllers, from logic-based symbolic systems Russell and Norvig, to fuzzy logic Saffiotti, and behavior-based systems Mataric and Michaud, The versatility of evolutionary algorithms allows them to be used with almost all of these systems, be it to find the best parameters or the best controller architecture.

Nevertheless, the ideal substrate for ER should constrain evolution as little as possible, in order to make it possible to scale up to designs of unbounded complexity.

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As ER aims to use as little prior knowledge as possible, this substrate should also be able to use raw inputs from sensors and send low-level commands to actuators. Given these requirements, artificial neural networks are currently the preeminent controller paradigm in ER. Feed-forward neural networks are known to be able to reproduce any function with arbitrary precision Cybenko, , and are well recognized tools in signal processing images, sound, etc.

Bishop, ; Haykin, and robot control Miller et al. With recurrent connections, neural networks can also approximate any dynamical system Funahashi and Nakamura, In addition, since neural networks are also used in models of the brain, ER can build on a vast body of research in neuroscience, for instance, on synaptic plasticity Abbott and Nelson, or network theory Bullmore and Sporns, There are many different kinds of artificial neural networks to choose from, and many ways to evolve them.

First, a neuron can be simulated at several levels of abstraction. Others implement leaky integrators Beer, , which take time to into account and may be better suited to dynamical systems these networks are sometimes called continuous-time recurrent neural networks, CTRNN.

More complex neuron models, e. Second, once the neuron model is selected, evolution can act on synaptic parameters, the architecture of the network, or both at the same time. In cases where evolution is applied only to synaptic parameters, common network topologies include feed-forward neural networks, Elman—Jordan networks [e. Fixing the topology, however, bounds the complexity of achievable behaviors. As a consequence, how to encode the topology and parameters of neural networks is one of the main open questions in ER.

How to encode a neural network so that it can generate a structure as complex, and also as organized, as a human brain? Many encodings have been proposed and tested, from direct encoding, in which evolution acts directly on the network itself Stanley and Miikkulainen, ; Floreano and Mattiussi, , to indirect encodings also called generative or developmental encodings , in which a genotype develops into the neural network Stanley and Miikkulainen, ; Floreano et al.

Most ER research, influenced by the vision of evolution as an optimization algorithm, relies on fitness functions, i. In this approach, the chosen fitness measure must increase, on average, from the very first solutions considered — which are in general randomly generated — toward the expected solution.

Typical fitness functions rely on performance criteria, and implicitly assume that increasing performance will lead the search in the direction of desired behaviors. Recent work has brought this assumption into question and shown that performance criteria can be misleading. Lehman and Stanley demonstrated in a set of experiments that using the novelty of a solution instead of the resulting performance on a task can actually lead to much better results.

In these experiments, the performance criterion was still used to recognize a good solution when it was discovered, but not to drive the search process. The main driver was the novelty of the solution with respect to previous exploration in a space of robot behavior features. Counter intuitively, driving the search process with the novelty of explored solutions in the space of behavioral features led to better results than driving the search with a performance-oriented measure, a finding that has emerged repeatedly in multiple contexts Lehman and Stanley, , ; Risi et al.

Many complex encodings have been proposed to evolve robot morphologies, control systems, or both [e. Unfortunately, these encodings did not enable the unbounded complexity that had been hoped for. There are two main reasons for this situation: 1 evolution is often prevented from exploring new ideas because it converges prematurely on a single family of designs, and 2 evolution selects individuals on the short term, whereas increases in complexity and organization are often only beneficial in the medium to long term. In a recent series of experiments, Mouret and Doncieux tested the relative importance of selective pressure and encoding in evolutionary robotics.

They compared a classic fitness function to approaches that modify the selective pressure to try to avoid premature convergence. They concluded that modifying the selective pressure made much more difference to the success of these experiments than changing the encoding. In a related field — evolution of networks — it has also been repeatedly demonstrated that the evolution of modular networks can be explained by the selective pressure alone, without the need for an encoding that can manipulate modules [ Kashtan and Alon , Espinosa-Soto and Wagner , Bongard , and Clune et al.

At the beginnings of ER, selective pressure was not a widely studied research theme. These encouraging results further suggest that a better understanding of selective pressures could help evolutionary robotics scale up to more complex tasks and designs. At any rate, while the future is likely to see more work on evolutionary pressures, selective pressures, and encodings need to act in concert to have a chance at leading to animal-like complexity Huizinga et al.

Figure 3. Recent work on selective pressures suggest that taking the behavior into account in the selection process is beneficial. In particular, it may be helpful, rather than only considering fitness as indicated by some specific quantitative performance measure, to also take into account aspects of robot behavior, which are less directly indexed to successful performance of a task, e.

Simulation is a valuable tool in evolutionary robotics because it makes it possible for researchers to quickly evaluate their ideas, easily replicate experiments, and share their experimental setup online. A natural follow-up idea is to evolve solutions in simulation, and then transfer the best ones to the final robot: evolution is fast, because it occurs in simulation, and the result is applied to real hardware, so it is useful.

Unfortunately, it is now well established that solutions evolved in simulation most often do not work on the real robot. It has at least been documented with Khepera-like robots obstacle avoidance, maze navigation Jakobi et al. The reality gap can be reduced by improving simulators, for instance, by using machine learning to model the sensors of the target robot Miglino et al. Jakobi et al. This function is learned by transferring a dozen controllers during the evolutionary process, and can be used to search for solutions that are both high-performing and well simulated e.

Another possible way to reduce the magnitude of the reality gap is to encourage the development of robust controllers. In this case, the differences between simulation and reality are seen as perturbations that the evolved controller should reject. More generally, it is possible to reward some properties of simulated behaviors so that evolved controllers are less likely to over-fit in simulation. For instance, Lehman et al. Last, controller robustness can be improved by adding online adaptation abilities, typically by evolving plastic neural networks Urzelai and Floreano, The reality gap can be completely circumvented by abandoning simulators altogether.

Some experiments in s thus evaluated the performance of each candidate solution using a robot in an arena, which was tracked with an external device and connected to an external computer Nolfi and Floreano, These experiments led to successful but basic behaviors, for instance, wall-following or obstacle avoidance. Successful experiments have also been reported for locomotion Hornby et al.

However, only a few hundreds of evaluations can be realistically performed with a robot: reality cannot be sped up, contrary to simulators, and real hardware wears out until it ultimately breaks. A promising approach to scale up to more complex behaviors is to use a population of robots, instead of a single one Watson et al. These different approaches to bridging the reality gap primarily aim at making ER possible on a short time scale. Where to start when designing the structure of a robot? Should it be randomly generated up to a certain complexity?

Should it start from the simplest structures and grow in complexity, or should it start from the most complex designs and be simplified over the generations? These questions have not yet been theoretically answered. One possibility is to start from a complex solution and then reduce its complexity. A neural network can thus begin fully connected and connections can be pruned afterwards through a connection selection process Changeux et al.

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But a growing body of evidence suggests that starting simple and progressively growing in complexity is a good idea. This is one of the main principles of NEAT Stanley and Miikkulainen, , which is now a reference in terms of neural network encoding. Associated with the principle of novelty search, it allows the evolutionary process to explore behaviors of increasing complexity, and has led to promising results Lehman and Stanley, Furthermore, progressively increasing the complexity of the robot controller, morphology, or evaluation conditions aligns with the principles of ecological balance proposed by Pfeifer and Bongard : the complexity of the different parts of a system should be balanced.

If individuals from the very first generation are expected to exhibit a simple behavior, it seems reasonable to provide them with the simplest context and structure. This is also consistent with different fields of biology, either from a phylogenetic point of view the first organisms were the simplest unicellular organisms or from an ontogenetic point of view [maturational constraints reduce the complexity of perception and action for infants Turkewitz and Kenny, ; Bjorklund, ].

In any case, there is a need for more theoretical studies on these questions, as the observation of these principles in biology may result from physical or biological constraints rather than from optimality principles. In this section, we give a treatment of the state of the art, organized around a number of open issues within evolutionary robotics. These issues are receiving much attention, and serving to drive further developments in the field.


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Real-world applications are not systematically followed by research papers advertising the approach taken by the engineers. It is thus hard to evaluate to what extent ER methods are currently used in this context. In any case, several successful examples of the use of ER methods in the context of real-world problems can be found in the literature. In these examples, ER methods were used either for one particular step in the design process or as a component of a larger learning architecture.

Hauert et al. Understanding how these behaviors work led to new insights into how to solve the problem. ER methods were used only in simulation, and more classical methods were used to implement the solutions in real robots.

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Macalpine et al. Neither of these approaches implements ER as a full-blown holistic design method, but they show that its principles and the corresponding algorithms now work well enough to be included in robot design processes. Using ER as a holistic approach to a real-world robotic problem remains a challenge because of the large number of evaluations that it implies. The perspective in this context is to rely either on many robots in parallel see Sections 5.