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In the field of evolutionary robotics, choosing the correct genetic representation is a complicated and delicate matter, especially when robots evolve behaviour and morphology at the same time. One principal problem is the lack of methods or tools to investigate and compare representations. In this paper we introduce and evaluate such a tool based on the biological notion of heritability. Heritability captures the proportion of phenotypic variation caused by genotypic variation and is often used to better understand the transmissibility of traits in real biological systems. As a proof of concept, we compare the heritability of various robot traits in two systems, one using a direct (tree based) representation and one using an indirect (grammar based) representation. We measure changes in heritability during the course of evolution and investigate how direct and indirect representation can be biased towards more exploration or exploitation throughout the course of evolution. The empirical study shows that heritability can be a useful tool to analyze different representations without running complete evolutionary processes using them. [ABSTRACT FROM AUTHOR]

A swarm of robots can collectively select an option among the available alternatives offered by the environment through a process known as collective decision-making. This process is characterised by the fact that once the group makes a decision, it can not be attributed to any of its group members. In swarm robotics, the individual mechanisms for collective decision-making are generally hand-designed and limited to a restricted set of solutions based on the voter or the majority model. In this paper, we demonstrate that it is possible to take an alternative approach in which the individual mechanisms are implemented using artificial neural network controllers automatically synthesised using evolutionary computation techniques. We qualitatively describe the group dynamics underlying the decision process on a collective perceptual discrimination task. We carry out extensive comparative tests that quantitatively evaluate the performance of the most commonly used decision-making mechanisms (voter model and majority model) with the proposed dynamic neural network model under various operating conditions and for swarms that differ in size. The results of our study clearly indicate that the performances of a swarm employing dynamical neural networks as the decision-making mechanism are more robust, more adaptable to a dynamic environment, and more scalable to a larger swarm size than the performances of the swarms employing the voter and the majority model. These results, generated in simulation, are ecologically validated on a swarm of physical e-puck2 robots. [ABSTRACT FROM AUTHOR]

This paper demonstrates a controller design of a multi-legged robotic swarm in a rough terrain environment. Many studies in swarm robotics are conducted with mobile robots that work in relatively flat fields. This paper focuses on a multi-legged robotic swarm, which is expected to operate not only in a flat field but also in rough terrain environments. However, designing a robot controller becomes a challenging problem because a designer has to consider how to coordinate a large number of joints in a robot, besides the complexity of a swarm problem. This paper employed an evolutionary robotics approach for the automatic design of a robot controller. The experiments were conducted by computer simulations with the path formation task. The results showed that the proposed approach succeeds in generating collective behavior in flat and rough terrain environments. [ABSTRACT FROM AUTHOR]

This paper focuses on generating collective behavior of a robotic swarm using an attention agent. The selective attention mechanism enables an agent to cope with environmental variations which are irrelevant to the task. This paper applies attention mechanisms to a robotic swarm for enhancing system-level properties, such as flexibility or scalability. To train an attention agent, evolutionary computations become a promising method, because a controller structure is not restricted by a gradient-based method. Therefore, this paper employs a deep neuroevolution approach to generating collective behavior in a robotic swarm. The experiments are conducted by computer simulations that consist of the Unity 3D game engine. The performance of the attention agent is compared with the convolutional neural network approach. The experimental results showed that the attention agent obtained generalization abilities in a robotic swarm similar to single-agent problems. [ABSTRACT FROM AUTHOR]

Embodied evolution is an evolutionary robotics approach that implements an evolutionary algorithm over a population of robots and evolves while the robots perform their tasks. So far, most studies on embodied evolution utilize relatively simple neural networks as robot controllers. However, a simple structured controller might restrict robot behavior and lead to lower performance. This paper proposes an embodied evolution approach that uses echo state networks as robot controllers. The experiments are conducted using computer simulations, and the controllers are evolved in a two-target navigation task. The results show that the echo state network controllers outperform the conventional controllers. [ABSTRACT FROM AUTHOR]

This paper demonstrates to generate a collective behavior of a multi-legged robotic swarm based on the evolutionary robotics approach. Most studies in swarm robotics are conducted using mobile robots driven by wheels. This paper focuses on generating collective behavior using a multi-legged robotic swarm. The evolutionary robotics approach is employed for designing a robot controller. The intuition-based constraint factors are incorporated into the fitness function to make the gait of robots similar to natural organisms. The experiment on a task of forming a line is conducted in computer simulations using the PyBullet physics engine. The robot controller is represented by a recurrent neural network with a single hidden layer. The experimental results show that proposed constraint factors successfully designed the robot's gait similar to natural organisms. The results also show that the evolutionary robotics approach successfully designed the robot controller for collective behavior of a multi-legged robotic swarm. [ABSTRACT FROM AUTHOR]

This paper deals with the topology and weight evolution of Neuro-Controllers (NCs). It focuses on meta-problems of developing motion controllers that can operate successfully in several given motion-problems, where each of these motion problems defers by the arena and task. Here, the evolution aims to find both specialized and non-specialized controllers. The non-specialized controllers are evolved to provide a successful motion for the entire set of the given motion-problems, whereas each specialized controller is evolved to be optimal in at least one of these problems. The meta-problem is defined as a many-objective optimization problem, in which the kth objective is to maximize the motion performance in the kth motion problem. Following the problem description, a decomposition-based evolutionary algorithm, which is termed NEWS/D, is presented. This algorithm, which has recently been proposed by the authors, is specially designed to allow the topology and weight evolution of NCs under a large number of objectives. Next, the algorithm is applied to find NCs for three meta-problems, i.e., three sets of motion problems. The results of the experiments show that the obtained three sets of NCs include both specialized and non-specialized controllers. In addition, the proposed approach of many-objective topology and weight evolution of NCs is analyzed in comparison with other approaches, including three versions of decomposition-based fixed topology optimization. It is shown here that for the same number of evaluations, NEWS/D achieved better results than those of the other approaches. Finally, a topology analysis is carried out for the specialized NCs, which suggests that the solution includes multiple equivalent NCs. The numerical implications of this phenomenon are left for future work. [ABSTRACT FROM AUTHOR]

This paper focuses on generating the collective step-climbing behavior of a multi-legged robotic swarm. Most studies on swarm robotics develop collective behaviors in a flat environment using mobile robots equipped with wheels. However, these types of robots could only show relatively simple behavior, which limits a task that could be addressed by a robotic swarm. This paper deals with a step-climbing task, in which a robotic swarm climbs a step that is too high for a single robot. The robots have to use other robots as a foothold to achieve the task. To generate such three-dimensional behavior, a robotic swarm is conducted using the multi-legged robot inspired by ants. The robot controller is obtained by the combination of the neuroevolution approach with manual designed methods. The results of the computer simulations show that the designed controller successfully achieve the step-climbing task. [ABSTRACT FROM AUTHOR]

This paper applies Topology and Weight Evolving Artificial Neural Networks (TWEANNs) to design controllers for a robotic swarm. A typical method of designing controllers by an evolutionary robotics approach uses neural networks as the robot controllers with only optimizing the weight values of the neural network. However, this approach might restrict the behavior of robots or might have unsuitable structures within the controller. In this paper, we applied Mutation-Based Evolving Artificial Neural Network (MBEANN), which is a TWEANN algorithm that only employs mutations to evolve neural networks, to design the controller for a robotic swarm. For comparison with the MBEANN approach, we used NeuroEvolution of Augmenting Topologies (NEAT), which is a widely used TWEANN algorithm. The controllers are evaluated in cooperative transport performed by a robotic swarm using computer simulations. The results show that the robot controller evolved with MBEANN outperformed the NEAT controller in the cooperative transportation task. [ABSTRACT FROM AUTHOR]

In evolutionary robotics, robot controllers are often evolved in simulation, as using the physical robot for fitness evaluation can take a prohibitively long time. Simulators provide a quick way to evaluate controller fitness. A simulator is tasked with providing appropriate sensor information to the controller. If the robot has an on-board camera, an entire virtual visual environment is needed to simulate the camera's signal. In the past, these visual environments have been constructed by hand, requiring the use of hand-crafted models, textures and lighting, which is a tedious and time-consuming process. This paper proposes a deep neural network-based architecture for simulating visual environments. The neural networks are trained exclusively from images captured from the robot, creating a 3-dimensional visual environment without using hand-crafted models, textures or lighting. It does not rely on any external domain specific datasets, as all training data is captured in the physical environment. Robot controllers were evolved in simulation to discern between objects with different colours and shapes, and they successfully completed the same task in the real world. [ABSTRACT FROM AUTHOR]

This paper explores current developments in evolutionary and bio-inspired approaches to autonomous robotics, concentrating on research from our group at the University of Sussex. These developments are discussed in the context of advances in the wider fields of adaptive and evolutionary approaches to AI and robotics, focusing on the exploitation of embodied dynamics to create behaviour. Four case studies highlight various aspects of such exploitation. The first exploits the dynamical properties of a physical electronic substrate, demonstrating for the first time how component-level analog electronic circuits can be evolved directly in hardware to act as robot controllers. The second develops novel, effective and highly parsimonious navigation methods inspired by the way insects exploit the embodied dynamics of innate behaviours. Combining biological experiments with robotic modeling, it is shown how rapid route learning can be achieved with the aid of navigation-specific visual information that is provided and exploited by the innate behaviours. The third study focuses on the exploitation of neuromechanical chaos in the generation of robust motor behaviours. It is demonstrated how chaotic dynamics can be exploited to power a goal-driven search for desired motor behaviours in embodied systems using a particular control architecture based around neural oscillators. The dynamics are shown to be chaotic at all levels in the system, from the neural to the embodied mechanical. The final study explores the exploitation of the dynamics of brain-body-environment interactions for efficient, agile flapping winged flight. It is shown how a multi-objective evolutionary algorithm can be used to evolved dynamical neural controllers for a simulated flapping wing robot with feathered wings. Results demonstrate robust, stable, agile flight is achieved in the face of random wind gusts by exploiting complex asymmetric dynamics partly enabled by continually changing wing and tail morphologies. [ABSTRACT FROM AUTHOR]

Theory of mind (ToM) is the ability to understand others' mental states (e.g., intentions). Studies on human ToM show that the way we understand others' mental states is very efficient, in the sense that observing only some portion of others' behaviors can lead to successful performance. Recently, ToM has gained interest in robotics to build robots that can engage in complex social interactions. Although it has been shown that robots can infer others' internal states, there has been limited focus on the data utilization of ToM mechanisms in robots. Here we show that robots can infer others' intentions based on limited information by selectively and flexibly using behavioral cues similar to humans. To test such data utilization, we impaired certain parts of an actor robot's behavioral information given to the observer, and compared the observer's performance under each impairment condition. We found that although the observer's performance was not perfect compared to when all information was available, it could infer the actor's mind to a degree if the goal-relevant information was intact. These results demonstrate that, similar to humans, robots can learn to infer others' mental states with limited information. [ABSTRACT FROM AUTHOR]

This paper focuses on the collective cognition by robotic swarms. The robotic swarms expected to perform tasks that are beyond the capability of a single robot by collective behavior that emerge from local interactions, similar to biological swarms. However, the robotic swarms have to rely on the collective cognition more than biological swarms when considering the limitation in sensory capabilities and the cost of each robot. In this paper, we develop controllers for a robotic swarm to accomplish a foraging task that requires collective cognition. In this task, robots have to both collectively distinguish two objects, namely food and poison, and cooperatively transport food objects to the nest. We applied an evolutionary robotics approach with the covariance matrix adaptation evolution strategy to develop controllers for robotic swarms. The results of computer simulations show that collective cognition was successfully developed, which allows the robots to transport only food objects. In addition, we also perform experiments to examine the scalability and the flexibility of the developed controllers. [ABSTRACT FROM AUTHOR]

Crowd navigation with autonomous systems is a topic which has seen a rapid increase in interest recently. While it appears natural to humans, being able to reach a target can prove difficult or impossible to a mobile robot because of the safety issues related to collisions with people. In this work we propose an approach to control a robot in a crowded environment; the method employs an Artificial Neural Network (ANN) that is trained with the NeuroEvolution of Augmented Topologies (NEAT) method. Models for the kinematics, perception, and cognition of the robot are presented. In particular, perception is based on a raycasting model which is tailored on the ANN. An in-depth analysis of a number of parameters of the environment and the robot is performed and a comparative analysis is presented; finally, results of the performance of the controller trained with NEAT are compared to those of a human driver who takes over the controller itself. Results show that the intelligent controller is able to perform on par with the human, within the simulated environment. [ABSTRACT FROM AUTHOR]

This paper focuses on the effect of the embodiment of robots on collective behavior in robotic swarms. The research field of swarm robotics emphasizes the importance of the embodiment of robots; however, only a few studies have discussed how it influences the collective behavior of a robotic swarm. In this paper, a path-formation task is performed by robotic swarms in computer simulations with and without considering collisions among robots to discuss the effect of the robot embodiment. Additionally, the experiments were performed with varying the size of robots. The robot controllers were obtained by an evolutionary robotics approach. The results show that the robot collisions would affect not only the performance of the robotic swarm but also the emergent behavior to accomplish the task. The robot collisions seem to provide feedback on robotic swarms to emerge the division of labor among robots to manage congestion. [ABSTRACT FROM AUTHOR]

The Evolutionary Robotics (ER) process has been applied extensively to developing control programs to achieve locomotion in legged robots, as an automated alternative to the arduous task of manually creating control programs for such robots. The evolution of such controllers is typically performed in simulation by making use of a physics engine-based robotic simulator. Making use of such physics-based simulators does, however, have certain challenges associated with it, such as these simulators' computational inefficiency, potential issues with lack of accuracy and the human effort required to construct such simulators. The current study therefore proposed and investigated an alternative method of simulation for a hexapod (six-legged) robot in the ER process, and directly compared this newly-proposed simulation method to traditional physics-based simulation. This alternative robotic simulator was built based solely on experimental data acquired directly from observing the behaviour of the robot. This data was used to construct a simulator for the robot based on Artificial Neural Networks (ANNs). To compare this novel simulation method to traditional physics simulation, the ANN-based simulators were used to evolve simple open-loop locomotion controllers for the robot in simulation. The real-world performance of these controllers was compared to that of controllers evolved in a more traditional physics-based simulator. The obtained results indicated that the use of ANN-based simulators produced controllers which could successfully perform the required locomotion task on the real-world robot. In addition, the controllers evolved using the ANN-based simulators allowed the real-world robot to move further than those evolved in the physics-based simulator and the ANN-based simulators were vastly more computationally efficient than the physics-based simulator. This study thus decisively indicated that ANN-based simulators offer a superior alternative to widely-used physics simulators in ER for the locomotion task considered. [ABSTRACT FROM AUTHOR]

This paper surveys various applications of artificial evolution in the field of modular robots. Evolutionary robotics aims to design autonomous adaptive robots automatically that can evolve to accomplish a specific task while adapting to environmental changes. A number of studies have demonstrated the feasibility of evolutionary algorithms for generating robotic control and morphology. However, a huge challenge faced was how to manufacture these robots. Therefore, modular robots were employed to simplify robotic evolution and their implementation in real hardware. Consequently, more research work has emerged on using evolutionary computation to design modular robots rather than using traditional hand design approaches in order to avoid cognition bias. These techniques have the potential of developing adaptive robots that can achieve tasks not fully understood by human designers. Furthermore, evolutionary algorithms were studied to generate global modular robotic behaviors including; self-assembly, self-reconfiguration, self-repair, and self-reproduction. These characteristics allow modular robots to explore unstructured and hazardous environments. In order to accomplish the aforementioned evolutionary modular robotic promises, this paper reviews current research on evolutionary robotics and modular robots. The motivation behind this work is to identify the most promising methods that can lead to developing autonomous adaptive robotic systems that require the minimum task related knowledge on the designer side. [ABSTRACT FROM AUTHOR]

A testbed has recently been introduced that evolves controllers for arbitrary hover-capable UAVs, with evaluations occurring directly on the robot. To prepare the testbed for real-world deployment, we investigate the effects of state-space limitations brought about by physical tethering (which prevents damage to the UAV during stochastic tuning), on the generality of the evolved controllers. We identify generalisation issues in some controllers, and propose an improved method that comprises two stages: in the first stage, controllers are evolved as normal using standard tethers, but experiments are terminated when the population displays basic flight competency. Optimisation then continues on a much less restrictive tether, effectively free-flying, and is allowed to explore a larger state-space envelope. We compare the two methods on a hover task using a real UAV, and show that more general solutions are generated in fewer generations using the two-stage approach. A secondary experiment undertakes a sensitivity analysis of the evolved controllers. [ABSTRACT FROM AUTHOR]

Heterogeneous multirobot systems have shown significant potential in many applications. Cooperative coevolutionary algorithms (CCEAs) represent a promising approach to synthesise controllers for such systems, as they can evolve multiple co-adapted components. Although CCEAs allow for an arbitrary level of team heterogeneity, in previous works heterogeneity is typically only addressed at the behavioural level. In this paper, we study the use of CCEAs to evolve control for a heterogeneous multirobot system where the robots have disparate morphologies and capabilities. Our experiments rely on a simulated task where a simple ground robot must cooperate with a complex aerial robot to find and collect items. We first show that CCEAs can evolve successful controllers for physically heterogeneous teams, but find that differences in the complexity of the skills the robots need to learn can impair CCEAs' effectiveness. We then study how different populations can use different evolutionary algorithms and parameters tuned to the agents' complexity. Finally, we demonstrate how CCEAs' effectiveness can be improved using incremental evolution or novelty-driven coevolution. Our study shows that, despite its limitations, coevolution is a viable approach for synthesising control for morphologically heterogeneous systems. [ABSTRACT FROM AUTHOR]

Swarm robotics is a field in which multiple robots coordinate their collective behavior autonomously to accomplish a given task without any form of centralized control. In swarm robotics, task allocation refers to the behavior resulting in robots being dynamically distributed over different sub-tasks, which is often required for solving complex tasks. It has been well recognized that evolutionary robotics is a promising approach to the development of collective behaviors for robotic swarms. However, the artificial evolution often suffers from two issues—the bootstrapping problem and deception—especially when the underlying task is profoundly complex. In this study, we propose a two-step scheme consisting of task partitioning and autonomous task allocation to overcome these difficulties. We conduct computer simulation experiments where robotic swarms have to accomplish a complex collective foraging problem, and the results show that the proposed approach leads to perform more effectively than a conventional evolutionary robotics approach. [ABSTRACT FROM AUTHOR]

Redundancy in the number of robots is a fundamental feature of robotic swarms to confer robustness, flexibility, and scalability. However, robots tend to interfere with each other in a case, where multiple robots gather in a spatially limited environment. The aim of this paper is to understand how a robotic swarm develops an effective strategy to manage congestion. The controllers of the robots are obtained by an evolutionary robotics approach. The strategy of managing congestion is observed in the process of generating a collective path of robots visiting two landmarks alternately. The robotic swarm exhibits autonomous specialization that the robots traveling inside the path activate the LEDs, while the robots in the outer side deactivate them. We found that the congestion is regulated in an emergent way of autonomous specialization by the result of an artificial evolution. [ABSTRACT FROM AUTHOR]

How do research fields evolve? This study confronts this question here by developing an inductive analysis based on emerging research fields of human microbiome, evolutionary robotics and astrobiology (also called exobiology). Data analysis considers papers associated with subject areas of authors from starting years to 2017 per each research field under study. Findings suggest some empirical properties of the evolution of research fields: the first property states that the evolution of a research field is driven by few disciplines (3-5) that generate more than 80% of documents (concentration of scientific production); the second property states that the evolution of research fields is path-dependent of critical disciplines: they can be parent disciplines that have originated the research field or new disciplines emerged during the evolution of science; the third property states that the evolution of research fields can be also due to a new discipline originated from a process of specialization within applied or basic sciences and/or convergence between disciplines. Finally, the fourth property states that the evolution of specific research fields can be due to both applied and basic sciences. These results here can explain and generalize some characteristics of the evolution of scientific fields in the dynamics of science. Overall, then, this study begins the process of clarifying and generalizing, as far as possible, the general properties of the evolution of research fields to lay a foundation for the development of sophisticated theories of the evolution of science. [ABSTRACT FROM AUTHOR]

Integration of Control Theory and Genetic Programming paradigm toward development a family of controllers is addressed in this paper. These controllers are applied for autonomous navigation with collision avoidance and bounded velocity of an omnidirectional mobile robot. We introduce the concepts of natural and adaptive behaviors to relate each control objective with a desired behavior for the mobile robot. Natural behaviors lead the system to fulfill a task inherently. In this work, the motion of the mobile robot to achieve desired position, ensured by applying a Control-Theory-based controller, defines the natural behavior. The adaptive behavior, learned through Genetic-Programming, fits the robot motion in order to avoid collision with an obstacle while fulfilling velocity constraints. Hence, the behavior of the mobile robot is the addition of the natural and the adaptive behaviors. Our proposed methodology achieves the discovery of 9402 behaviors without collisions where asymptotic convergence to desired goal position is demonstrated by Lyapunov stability theory. Effectiveness of proposed framework is illustrated through a comparison between experiments and numerical simulations for a real mobile robot. [ABSTRACT FROM AUTHOR]

The concept of affordances has been used in robotics to model action opportunities of a robot and as a basis for making decisions involving objects. Affordances capture the interdependencies between the objects and their properties, the executed actions on those objects, and the effects of those respective actions. However, existing affordance models cannot cope with multiple objects that may interact during action execution. Our approach is unique in that possesses the following four characteristics. First, our model employs recent advances in probabilistic programming to learn affordance models that take into account (spatial) relations between different objects, such as relative distances. Two-object interaction models are first learned from the robot interacting with the world in a behavioural exploration stage, and are then employed in worlds with an arbitrary number of objects. The model thus generalizes over both the number of and the particular objects used in the exploration stage, and it also effectively deals with uncertainty. Secondly, rather than using a (discrete) action repertoire, the actions are parametrised according to the motor capabilities of the robot, which allows to model and achieve goals at several levels of complexity. It also supports a two-arm parametrised action. Thirdly, the relational affordance model represents the state of the world using both discrete (action and object features) and continuous (effects) random variables. The effects follow a multivariate Gaussian distribution with the correlated discrete variables (actions and object properties). Fourthly, the learned model can be employed on planning for high-level goals that closely correspond to goals formulated in natural language. The goals are specified by means of (spatial) relations between the objects. The model is evaluated in real experiments using an iCub robot given a series of such planning goals of increasing difficulty. [ABSTRACT FROM AUTHOR]

In this work the center-crossing condition was integrated in artificial neural networks that incorporate synaptic delays in their connections. These synaptic delay based neural networks act as Central Pattern Generators (CPGs) for walking controllers in hexapod robotic structures. Simulated evolution is used to automatically obtain such neural controllers for walking behaviors. The optimized controllers show the time reasoning capabilities of the synaptic delay based neural networks for the temporal coordination of the hexapod joints. We compared the results against continuous time recurrent neural networks, one of the neural models most used as CPG, when proprioceptive information is used to provide fault tolerance for the required behavior. [ABSTRACT FROM AUTHOR]

The inverted pendulum control problem is a classical benchmark in control theory. Amongst the approaches to developing control programs for an inverted pendulum, the evolution of Artificial Neural Network (ANN) based controllers has received some attention. The authors have previously shown that Evolutionary Robotics (ER) can successfully be used to evolve inverted pendulum stabilization controllers in simulation and that these controllers can transfer successfully from simulation to real-world robotic hardware. During this process, use was made of robotic simulators constructed from empirically-collected data and based on ANNs. The current work aims to compare this method of simulator construction with the more traditional method of building robotic simulators based on physics equations governing the robotic system under consideration. In order to compare ANN-based and physics-based simulators in the evolution of inverted pendulum controllers, a real-world wheeled inverted pendulum robot was considered. Simulators based on ANNs as well as on a system of ordinary differential equations describing the dynamics of the robot were developed. These two simulation techniques were then compared by using each in the simulation-based evolution of controllers. During the evolution process, the effects of injecting different levels of noise into the simulation was furthermore studied. Encouraging results were obtained, with controllers evolved using ANN-based simulators and realistic levels of noise outperforming those evolved using the physics-based simulators. [ABSTRACT FROM AUTHOR]

In this paper, we show how the development of plastic behaviours, i.e., behaviour displaying a modular organisation characterised by behavioural subunits that are alternated in a context-dependent manner, can enable evolving robots to solve their adaptive task more efficiently also when it does not require the accomplishment of multiple conflicting functions. The comparison of the results obtained in different experimental conditions indicates that the most important prerequisites for the evolution of behavioural plasticity are: the possibility to generate and perceive affordances (i.e., opportunities for behaviour execution), the possibility to rely on flexible regulatory processes that exploit both external and internal cues, and the possibility to realise smooth and effective transitions between behaviours. [ABSTRACT FROM AUTHOR]

We extend an abstract agent-based swarming model based on the evolution of neural network controllers, to explore further the emergence of swarming. Our model is grounded in the ecological situation, in which agents can access some information from the environment about the resource location, but through a noisy channel. Swarming critically improves the efficiency of group foraging, by allowing agents to reach resource areas much more easily by correcting individual mistakes in group dynamics. As high levels of noise may make the emergence of collective behavior depend on a critical mass of agents, it is crucial to reach sufficient computing power to allow for the evolution of the whole set of dynamics in simulation. Since simulating neural controllers and information exchanges between agents are computationally intensive, to scale up simulations to model critical masses of individuals, the implementation requires careful optimization. We apply techniques from astrophysics known as treecodes to compute the signal propagation, and efficiently parallelize for multi-core architectures. Our results open up future research on signal-based emergent collective behavior as a valid collective strategy for uninformed search over a domain space. [ABSTRACT FROM AUTHOR]

In recent years, a significant evolution of forecasting methods has been possible due to advances in artificial computational intelligence. The achievement of the optimal architecture of an ANN is a complex process. Thus, in this work, an Evolutionary Robotic (study of the evolution of an ANN using Genetic Algorithm) approach has been used to obtain an Artificial Neuro-Genetic Networks (ANGN) to the short-term forecasting of daily irrigation water demand that maximizes the accuracy of the predictions. The methodology is applied in the Bembézar Irrigation District (Southern Spain). An optimal ANGN architecture (ANGN (7, 29, 16, 1)) has achieved obtaining a Standard Error Prediction (SEP) value of the daily water demand of 12.63 % and explaining 93 % of the total variance observed during validation process. The developed model proved to be a powerful tool that, without long dataset and time requirements, can be very useful for the development of management strategies. [ABSTRACT FROM AUTHOR]

We explored the role of modularity as a means to improve evolvability in populations of adaptive agents. We performed two sets of artificial life experiments. In the first, the adaptive agents were neural networks controlling the behavior of simulated garbage collecting robots, where modularity referred to the networks architectural organization and evolvability to the capacity of the population to adapt to environmental changes measured by the agents performance. In the second, the agents were programs that control the changes in network's synaptic weights (learning algorithms), the modules were emerged clusters of symbols with a well defined function and evolvability was measured through the level of symbol diversity across programs. We found that the presence of modularity (either imposed by construction or as an emergent property in a favorable environment) is strongly correlated to the presence of very fit agents adapting effectively to environmental changes. In the case of learning algorithms we also observed that character diversity and modularity are also strongly correlated quantities. [ABSTRACT FROM AUTHOR]

Mainstream approaches to modelling cognitive processes have typically focused on (1) reproducing their neural underpinning, without regard to sensory-motor systems and (2) producing a single, ideal computational model. Evolutionary robotics is an alternative possibility to bridge the gap between neural substrate and behavior by means of a sensory-motor apparatus, and a powerful tool to build a population of individuals rather than a single model. We trained 4 populations of neurorobots, equipped with a pan/tilt/zoom camera, and provided with different types of motor control in order to perform a cancellation task, often used to tap spatial cognition. Neurorobots' eye movements were controlled by (a) position, (b) velocity, (c) simulated muscles and (d) simulated muscles with fixed level of zoom. Neurorobots provided with muscle and velocity control showed better performances than those controlled in position. This is an interesting result since muscle control can be considered a particular type of position control. Finally, neurorobots provided with muscle control and zoom outperformed those without zooming ability. [ABSTRACT FROM AUTHOR]

Evolutionary robotics (ER) has emerged as a fast growing field in the last two decades and has earned the attention of a number of researchers. Principles of biological evolution are applied in the form of evolutionary techniques for solving the complicated problems in the areas of robotic design and control. The diversity and the intensity of this growing field is presented in this paper through the contributions made by several researchers in the categories of robot controller design, robot body design, co-evolution of body and brain and in transforming the evolved robots in physical reality. The paper discusses some of the recent achievements in each of these fields along with some expected applications which are likely to motivate the future research. For the quick reference of the readers, a digest of all the works is presented in the paper, spanning the years and the areas of the research contributions. [ABSTRACT FROM AUTHOR]

We propose an approach to the synthesis of hierarchical control systems comprising both evolved and manually programmed control for autonomous robots. We recursively divide the goal task into sub-tasks until a solution can be evolved or until a solution can easily be programmed by hand. Hierarchical composition of behavior allows us to overcome the fundamental challenges that typically prevent evolutionary robotics from being applied to complex tasks: bootstrapping the evolutionary process, avoiding deception, and successfully transferring control evolved in simulation to real robotic hardware. We demonstrate the proposed approach by synthesizing control systems for two tasks whose complexity is beyond state of the art in evolutionary robotics. The first task is a rescue task in which all behaviors are evolved. The second task is a cleaning task in which evolved behaviors are combined with a manually programmed behavior that enables the robot to open doors in the environment. We demonstrate incremental transfer of evolved control from simulation to real robotic hardware, and we show how our approach allows for the reuse of behaviors in different tasks. [ABSTRACT FROM AUTHOR]

Many approaches to AI in robotics use a multi-layered approach to determine levels of behaviour from basic operations to goal-directed behaviour, the most well-known of which is the subsumption architecture. In this paper, the performances of the unigenic Gene Expression Programming (ugGEP) and multigenic GEP (mgGEP) in evolving robot controllers for a wall following robot are analysed. Additionally, the paper introduces Regulatory Multigenic Gene Expression Programming, a new evolutionary technique that can be utilised to automatically evolve modularity in robot behaviour. The proposed technique extends the mgGEP algorithm, by incorporating a regulatory gene as part of the GEP chromosome. The regulatory gene, just as in systems biology, determines which of the genes in the chromosome to express and therefore how the controller solves the problem. In the initial experiments, the proposed algorithm is implemented for a robot wall following problem and the results compared to that of ugGEP and mgGEP. In addition to the wall following behaviour, a robot foraging behaviour is implemented with the aim of investigating whether the position of a specific module (sub-expression tree) in the overall expression tree is of importance when coding for a problem. [ABSTRACT FROM AUTHOR]

Background: The biliopancreatic diversion with duodenal switch (BPD/DS) requires operating in three different abdominal quadrants. Previous techniques have used either two docks or a hybrid technique in which the robot is used only to suture the duodeno-ileal anastomosis, while the rest of the operation was performed laparoscopically. Recently, a modification in technique has allowed all operative steps to be completed robotically with a single dock. The operative technique and its technical results are described. Methods: Operative technique is described. Baseline demographics, operative duration, length of stay, and adverse events (intraoperative, 30-days, and 1-year) of all primary totally robot BPD/DS cases are reported. Results: From Nov. 2011 to Jan. 2014, 59 totally robotic BPD/DS operations were attempted. One was completed hybrid, and the rest were totally robotic. No robotic operation was converted to an open operation. Five trocars were placed, the small bowel was anchored to the anterior abdominal wall, and the robot was docked. Mean age was 44 ± 10 years with a mean preoperative BMI of 56 ± 9 kg/m. 69 % was female, and 71 % was Caucasian. Mean operative duration was 306 ± 80 min (60 min less than the hybrid technique). There were no mortality, leaks, venous thromboembolism, or bleeding requiring transfusion. Mean length of stay was 4.6 ± 4.3 days. Three patients were readmitted for nausea and vomiting. There was one superficial wound infection, and three patients needed reoperations in the first year, two for strictures, and one for debriding a suture abscess. Conclusions: All key technical components of the BPD/DS were performed with low morbidity and mortality with a single dock. Since the surgeon performed all key parts of the operation from the console, the need for experienced bedside assistance was minimized, resulting in shorter operative duration compared to the hybrid technique. [ABSTRACT FROM AUTHOR]

On-line evolution of robot controllers allows robots to adapt while they perform their proper tasks. In our investigations, robots contain their own self-sufficient evolutionary algorithm (known as the encapsulated approach) where individual solutions are evaluated by means of a time sharing scheme: an individual controller is given the run of the robot for some amount of time and fitness corresponds to the robot's task performance during that period. In this paper, we propose and provide a detailed analysis of two on-the-fly control schemes to set the evaluation time in highly dynamic scenarios with completely different tasks. One scheme, called the roulette-wheel selection scheme, stochastically selects evaluation time from promising intervals similar to multi-armed bandit schemes. The other scheme, named Heuristic-Rule (H-Rule), tweaks the evaluation time using specific heuristics. Our experiments show that H-Rule gives stable performance in different scenarios and can serve as a viable alternative to pre-selected optimal evaluation time. [ABSTRACT FROM AUTHOR]

This study compares two different evolutionary approaches (clonal and aclonal) to the design of homogeneous two-robot teams (i.e. teams of morphologically identical agents with identical controllers) in a task that requires the agents to specialise to different roles. The two approaches differ mainly in the way teams are formed during evolution. In the clonal approach, a team is formed from a single genotype within one population of genotypes. In the aclonal approach, a team is formed from multiple genotypes within one population of genotypes. In both cases, the goal is the synthesis of individual generalist controllers capable of integrating role execution and role allocation mechanisms for a team of homogeneous robots. Our results diverge from those illustrated in a similar comparative study, which supports the superiority of the aclonal versus the clonal approach. We question this result and its theoretical underpinning, and we bring new empirical evidence showing that the clonal outperforms the aclonal approach in generating homogeneous teams required to dynamically specialise for the benefit of the team. The results of our study suggest that task-specific elements influence the evolutionary dynamics more than the genetic relatedness of the team members. We conclude that the appropriateness of the clonal approach for role allocation scenarios is mainly determined by the specificity of the collective task, including the evaluation function, rather than by the way in which the solutions are evaluated during evolution. [ABSTRACT FROM AUTHOR]

The field of collective robotics has been raising increasing interest in the last few years. In the vast majority of works devoted to collective robotics all interacting robots play always the same function, while less attention has been paid to groups of collaborating robots in which different robots play different roles. In this paper we evolve a population of homogeneous robots for dynamically allocating roles through communicative interactions. In particular, we focus on the development of a team of robots in which one and only one individual (the 'leader') must differentiate its communicative behaviour from that of all the others ('non-leaders'). Evolved solutions prove to be very robust with respect to changes in the size of the group. Furthermore, both behavioural analyses and a comparison with a control condition in which robots are not allowed to move demonstrate the importance of co-adapting communicative and non-communicative behaviours, and, in particular, of being allowed to dynamically change the topology of communicative interactions. Finally, we show how the same method can be used for solving other kinds of role-allocation tasks. The general idea proposed in this paper might be used in the future for evolving general, robust, and scalable role differentiation mechanisms which can be exploited to develop non-communicative collaborative behaviours that require specialisation of roles within groups of homogeneous individuals. [ABSTRACT FROM AUTHOR]

This paper presents a behavioral ontogeny for artificial agents based on the interactive memorization of sensorimotor invariants. The agents are controlled by continuous timed recurrent neural networks (CTRNNs) which bind their sensors and motors within a dynamic system. The behavioral ontogenesis is based on a phylogenetic approach: memorization occurs during the agent's lifetime and an evolutionary algorithm discovers CTRNN parameters. This shows that sensorimotor invariants can be durably modified through interaction with a guiding agent. After this phase has finished, agents are able to adopt new sensorimotor invariants relative to the environment with no further guidance. We obtained these kinds of behaviors for CTRNNs with 3-6 units, and this paper examines the functioning of those CTRNNs. For instance, they are able to internally simulate guidance when it is externally absent, in line with theories of simulation in neuroscience and the enactive field of cognitive science. [ABSTRACT FROM AUTHOR]

Evolutionary robotics (ER) is often viewed as the application of a family of black-box optimization algorithms-evolutionary algorithms-to the design of robots, or parts of robots. When considering ER as black-box optimization, the selective pressure is mainly driven by a user-defined, black-box fitness function, and a domain-independent selection procedure. However, most ER experiments face similar challenges in similar setups: the selective pressure, and, in particular, the fitness function, is not a pure user-defined black box. The present review shows that, because ER experiments share common features, selective pressures for ER are a subject of research on their own. The literature has been split into two categories: goal refiners, aimed at changing the definition of a good solution, and process helpers, designed to help the search process. Two sub-categories are further considered: task-specific approaches, which require knowledge on how to solve the task and task-agnostic ones, which do not need it. Besides highlighting the diversity of the approaches and their respective goals, the present review shows that many task-agnostic process helpers have been proposed during the last years, thus bringing us closer to the goal of a fully automated robot behavior design process. [ABSTRACT FROM AUTHOR]

We propose and study a decentralized formation flying control architecture based on the evolutionary robotic technique. We develop our control architecture for the MIT SPHERES robotic platform on board the International Space Station and we show how it is able to achieve micrometre and microradians precision at the path planning level. Our controllers are homogeneous across satellites and do not make use of labels (i.e. all satellites can be exchanged at any time). The evolutionary process is able to produce homogeneous controllers able to plan, with high precision, for the acquisition and maintenance of any triangular formation. [ABSTRACT FROM AUTHOR]

We investigate the reality gap, specifically the environmental correspondence of an on-board simulator. We describe a novel distributed co-evolutionary approach to improve the transference of controllers that co-evolve with an on-board simulator. A novelty of our approach is the the potential to improve transference between simulation and reality without an explicit measurement between the two domains. We hypothesise that a variation of on-board simulator environment models across many robots can be competitively exploited by comparison of the real controller fitness of many robots. We hypothesise that the real controller fitness values across many robots can be taken as indicative of the varied fitness in environmental correspondence of on-board simulators, and used to inform the distributed evolution an on-board simulator environment model without explicit measurement of the real environment. Our results demonstrate that our approach creates an adaptive relationship between the on-board simulator environment model, the real world behaviour of the robots, and the state of the real environment. The results indicate that our approach is sensitive to whether the real behavioural performance of the robot is informative on the state real environment. [ABSTRACT FROM AUTHOR]

This paper is concerned with the problem of learning multiple skills by modular robots. The main question we address is whether it is better to learn multiple skills simultaneously (all-at-once) or incrementally (one-by-one). We conduct an experimental study with modular robots of various morphologies that need to acquire three different but correlated skills, efficient locomotion, navigation towards a target point, and obstacle avoidance, using a real-time, on-board evolution as the learning method. The results indicate that the one-by-one strategy is more efficient and more stable than the all-at-once strategy. [ABSTRACT FROM AUTHOR]