On the evolution of homogeneous two-robot teams: clonal versus aclonal approaches

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

Cooperative object transport with a swarm of e-puck robots: robustness and scalability of evolved collective strategies

Cooperative object transport in distributed multi-robot systems requires the coordination and synchronisation of pushing/pulling forces by a group of autonomous robots in order to transport items that cannot be transported by a single agent. The results of this study show that fairly robust and scalable collective transport strategies can be generated by robots equipped with a relatively simple sensory apparatus (i.e. no force sensors and no devices for direct communication). In the experiments described in this paper, homogeneous groups of physical e-puck robots are required to coordinate and synchronise their actions in order to transport a heavy rectangular cuboid object as far as possible from its starting position to an arbitrary direction. The robots are controlled by dynamic neural networks synthesised using evolutionary computation techniques. The best evolved controller demonstrates an effective group transport strategy that is robust to variability in the physical characteristics of the object (i.e. object mass and size of the longest object’s side) and scalable to different group sizes. To run these experiments, we designed, built, and mounted on the robots a new sensor that returns the agents’ displacement on a 2D plane. The study shows that the feedback generated by the robots’ sensors relative to the object’s movement is sufficient to allow the robots to coordinate their efforts and to sustain the transports for an extended period of time. By extensively analysing successful behavioural strategies, we illustrate the nature of the operational mechanisms underpinning the coordination and synchronisation of actions during group transport

Simulated road following using neuroevolution

This paper describes a methodology wherein genetic algorithms were used to evolve neural network controllers for application in automatic road driving. The simulated controllers were capable of dynamically varying the mixture of colour components in the input image to ensure the ability to perform well across the entire range of possible environments. During the evolution phase, they were evaluated in a set of environments carefully designed to encourage the development of flexible and general-purpose solutions. Successfully evolved controllers were capable of navigating simulated roads across challenging test environments, each with different geometric and colour distribution properties. These controllers proved to be more robust and adaptable compared to the previous work done using this evolutionary approach. This was due to their improved dynamic colour perception capabilities, as they were now able to demonstrate feature extraction in three (red, green and blue) colour channels

Swarm Cognition and Artificial Life

Abstract. Swarm Cognition is the juxtaposition of two relatively un-related concepts that evoke, on the one hand, the power of collective behaviours displayed by natural swarms, and on the other hand the com-plexity of cognitive processes in the vertebrate brain. Recently, scientists from various disciplines suggest that, at a certain level of description, op-erational principles used to account for the behaviour of natural swarms may turn out to be extremely powerful tools to identify the neuroscien-tific basis of cognition. In this paper, we review the most recent studies in this direction, and propose an integration of Swarm Cognition with Artificial Life, identifying a roadmap for a scientific and technological breakthrough in Cognitive Sciences.

On the Design of Generalist Strategies for Swarms of Simulated Robots Engaged in Task-allocation Scenarios

This study focuses on issues related to the evolutionary design of task-allocation mechanisms for swarm robotics systems with agents potentially capable of performing different tasks. Task allocation in swarm robotics refers to a process that results in the distribution of robots to different concurrent tasks without any central or hierarchical control. In this paper, we investigate a scenario with two concurrent tasks (i.e. foraging and nest patrolling) and two environments in which the task priorities vary. We are interested in generating successful groups made of behaviourally plastic agents (i.e. agents that are capable of carrying out different tasks in different environmental conditions), which could adapt their task preferences to those of their group mates as well as to the environmental conditions. We compare the results of three different evolutionary design approaches, which differ in terms of the agents’ genetic relatedness (i.e. groups of clones and groups of unrelated individuals), and/or the selection criteria used to create new populations (i.e. single and multi-objective evolutionary optimisation algorithms). We show results indicating that the evolutionary approach based on the use of genetically unrelated individuals in combination with a multi-objective evolutionary optimisation algorithm has a better success rate then an evolutionary approach based on the use of genetically related agents. Moreover, the multi-objective approach, when compared to a single-objective approach and genetically unrelated individual, significantly limits the tendency towards task specialisation by favouring the emergence of generalist agents without introducing extra computational costs. The significance of this result is discussed in view of the relationship between individual behavioural skills and swarm effectiveness

Evolutionary Robotics: a new scientific tool for studying cognition

We survey developments in Artificial Neural Networks, in Behaviour-based Robotics and Evolutionary Algorithms that set the stage for Evolutionary Robotics in the 1990s. We examine the motivations for using ER as a scientific tool for studying minimal models of cognition, with the advantage of being capable of generating integrated sensorimotor systems with minimal (or controllable) prejudices. These systems must act as a whole in close coupling with their environments which is an essential aspect of real cognition that is often either bypassed or modelled poorly in other disciplines. We demonstrate with three example studies: homeostasis under visual inversion; the origins of learning; and the ontogenetic acquisition of entrainment

From symbol grounding to socially shared embodied language knowledge

Much language-related research in cognitive robotics appeals to usage-based models of language as proposed in cognitive linguistics and developmental psychology [1, 2] that emphasise the significance of learning, embodiment and general cognitive development for human language acquisition. Over and above these issues, however, what takes centre stage in these theories are social-cognitive skills of “intention-reading” that are seen as “primary in the language acquisition process” [1] – and also as difficult to incorporate into computational models of language acquisition. The present paper addresses these concerns: we describe work in progress on a series of experiments that take steps towards closing the gap between ‘solipsistic’ symbol grounding in individual robotic agents and socially framed embodied language acquisition in learners that attend to common ground [3] with changing interlocutors

Design and analysis of proximate mechanisms for cooperative transport in real robots

This paper describes a set of experiments in which a homogeneous group of real e-puck robots is required to coordinate their actions in order to transport cuboid objects that are too heavy to be moved by single robots. The agents controllers are dynamic neural networks synthesised through evolutionary computation techniques. To run these experiments, we designed, built, and mounted on the robots a new sensor that returns the agent displacement on the x/y plane. In this object transport scenario, this sensor generates useful feedback on the consequences of the robot actions, helping the robots to perceive whether their pushing forces are aligned with the object movement. The results of our experiments indicated that the best evolved controller can effectively operate on real robots. The group transport strategies turned out to be robust and scalable to effectively operate in a variety of conditions in which we vary physical characteristics of the object and group cardinality. From a biological perspective, the results of this study indicate that the perception of the object movement could explain how natural organisms manage to coordinate their actions to transport heavy items