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Evolving Modularity in Soft Robots Through an Embodied and Self-Organizing Neural Controller

ARTIFICIAL LIFE 2022年第3期28卷 322-347页

作者： Pigozzi, Federico Medvet, Eric Univ Trieste Dept Engn & Architecture Trieste Italy

Modularity is a desirable property for embodied agents, as it could foster their suitability to different domains by disassembling them into transferable modules that can be reassembled differently. We focus on a class of embodied agents known as voxel-based soft robots (VSRs). They are aggregations of elastic blocks of soft material;as such, their morphologies are intrinsically modular. Nevertheless, controllers used until now for VSRs act as abstract, disembodied processing units: Disassembling such controllers for the purpose of module transferability is a challenging problem. Thus, the full potential of modularity for VSRs still remains untapped. In this work, we propose a novel self-organizing, embodied neural controller for VSRs. We optimize it for a given task and morphology by means of evolutionary computation: While evolving, the controller spreads across the VSR morphology in a way that permits emergence of modularity. We experimentally investigate whether such a controller (i) is effective and (ii) allows tuning of its degree of modularity, and with what kind of impact. To this end, we consider the task of locomotion on rugged terrains and evolve controllers for two morphologies. Our experiments confirm that our self-organizing, embodied controller is indeed effective. Moreover, by mimicking the structural modularity observed in biological neural networks, different levels of modularity can be achieved. Our findings suggest that the self-organization of modularity could be the basis for an automatic pipeline for assembling, disassembling, and reassembling embodied agents.

关键词： Embodied AI self-organization evolutionary robotics neuroevolution modularity

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Surprise versus unsurprise: Implications of emergence in robotics

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robotics AND AUTONOMOUS SYSTEMS 2001年第1期37卷 19-24页

作者： Ronald, EMA Sipper, M Ecole Polytech Ctr Math Appl F-91128 Palaiseau France Swiss Fed Inst Technol Log Syst Lab CH-1015 Lausanne Switzerland

We examine the eventual role of surprise in two domains of human endeavor: classical engineering and what we call "emergent engineering", with examples relevant to the field of robotics. Placing ourselves within the formal framework of the recently proposed "emergence test", we argue that the element of surprise, central in the test, serves to illuminate a fundamental difference between industrial and autonomous robots: unsurprise is demanded of classically engineered automation, while a mild form of surprise - unsurprising surprise - must of necessity be tolerated in biologically inspired systems, including behavior-based robotics. (C) 2001 Published by Elsevier Science B.V.

关键词： emergence test emergent engineering evolutionary robotics artificial neural networks

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Self-organisation and communication in groups of simulated and physical robots

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BIOLOGICAL CYBERNETICS 2006年第3期95卷 213-231页

作者： Trianni, Vito Dorigo, Marco Univ Libre Bruxelles IRIDIA B-1050 Brussels Belgium

In social insects, both self-organisation and communication play a crucial role for the accomplishment of many tasks at a collective level. Communication is performed with different modalities, which can be roughly classified into three classes: indirect (stigmergic) communication, direct interactions and direct communication. The use of stigmergic communication is predominant in social insects (e.g. the pheromone trails in ants), where, however, direct interactions (e.g. antennation in ants) and direct communication (e.g. the waggle dance in honey bees) can also be observed. Taking inspiration from insect societies, we present an experimental study of self-organising behaviours for a group of robots, which exploit communication to coordinate their activities. In particular, the robots are placed in an arena presenting holes and open borders, which they should avoid while moving coordinately. Artificial evolution is responsible for the synthesis in a simulated environment of the robot's neural controllers, which are subsequently tested on physical robots. We study different communication strategies among the robots: no direct communication, handcrafted signalling and a completely evolved approach. We show that the latter is the most efficient, suggesting that artificial evolution can produce behaviours that are more adaptive than those obtained with conventional design methodologies. Moreover, we show that the evolved controllers produce a self-organising system that is robust enough to be tested on physical robots, notwithstanding the huge gap between simulation and reality.

关键词： swarm robotics evolutionary robotics self-organisation swarm intelligence swarm-bot

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Communication based dynamic role allocation in a group of homogeneous robots

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NATURAL COMPUTING 2014年第3期13卷 391-402页

作者： Gigliotta, Onofrio Mirolli, Marco Nolfi, Stefano Univ Naples Federico II Dept Humanities Nac Lab Naples Italy Inst Cognit Sci & Technol Rome Italy

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.

关键词： evolutionary robotics Dynamic role allocation Neural controllers

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Synchronisation effects on the behavioural performance and information dynamics of a simulated minimally cognitive robotic agent

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BIOLOGICAL CYBERNETICS 2012年第6-7期106卷 407-427页

作者： Moioli, Renan C. Vargas, Patricia A. Husbands, Phil Univ Sussex CCNR Dept Informat Brighton BN1 9QH E Sussex England Heriot Watt Univ Sch Math & Comp Sci Edinburgh EH14 4AS Midlothian Scotland

Oscillatory activity is ubiquitous in nervous systems, with solid evidence that synchronisation mechanisms underpin cognitive processes. Nevertheless, its informational content and relationship with behaviour are still to be fully understood. In addition, cognitive systems cannot be properly appreciated without taking into account brain-body- environment interactions. In this paper, we developed a model based on the Kuramoto Model of coupled phase oscillators to explore the role of neural synchronisation in the performance of a simulated robotic agent in two different minimally cognitive tasks. We show that there is a statistically significant difference in performance and evolvability depending on the synchronisation regime of the network. In both tasks, a combination of information flow and dynamical analyses show that networks with a definite, but not too strong, propensity for synchronisation are more able to reconfigure, to organise themselves functionally and to adapt to different behavioural conditions. The results highlight the asymmetry of information flow and its behavioural correspondence. Importantly, it also shows that neural synchronisation dynamics, when suitably flexible and reconfigurable, can generate minimally cognitive embodied behaviour.

关键词： Synchronisation evolutionary robotics Oscillatory networks Transfer entropy Kuramoto model

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Evolving robot sub-behaviour modules using Gene Expression Programming

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GENETIC PROGRAMMING AND EVOLVABLE MACHINES 2015年第2期16卷 95-131页

作者： Mwaura, Jonathan Keedwell, Ed Univ Pretoria Dept Comp Sci ZA-0002 Pretoria South Africa Univ Exeter Coll Engn Math & Phys Sci Exeter Devon England

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.

关键词： Gene Expression Programming Subsumption architecture Layered learning evolutionary robotics Robot behaviour coordination

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Evolving controllers for high-level applications on a service robot: a case study with exhibition visitor flow control

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GENETIC PROGRAMMING AND EVOLVABLE MACHINES 2012年第2期13卷 239-263页

作者： Fukunaga, Alex Hiruma, Hideru Komiya, Kazuki Iba, Hitoshi Univ Tokyo Tokyo Japan

We investigate the application of simulation-based genetic programming to evolve controllers that perform high-level tasks on a service robot. As a case study, we synthesize a controller for a guide robot that manages the visitor traffic flow in an exhibition space in order to maximize the enjoyment of the visitors. We used genetic programming in a low-fidelity simulation to evolve a controller for this task, which was then transferred to a service robot. An experimental evaluation of the evolved controller in both simulation and on the actual service robot shows that it performs well compared to hand-coded heuristics, and performs comparably to a human operator.

关键词： Genetic programming evolutionary robotics Service robotics Applications

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QED: Using Quality-Environment-Diversity to Evolve Resilient Robot Swarms

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IEEE TRANSACTIONS ON evolutionary COMPUTATION 2021年第2期25卷 346-357页

作者： Bossens, David M. Tarapore, Danesh Univ Southampton Sch Elect & Comp Sci Southampton SO17 1BJ Hants England

In quality-diversity algorithms, the behavioral diversity metric is a key design choice that determines the quality of the evolved archives. Although behavioral diversity is traditionally obtained by describing the observed resulting behavior of robot controllers evaluated in a single environment, it is often more easily induced by introducing environmental diversity, i.e., by manipulating the environments in which the controllers are evaluated. This article proposes quality-environment-diversity (QED), an algorithm that repeatedly generates a random environment according to a probability distribution over environmental features (e.g., number of obstacles, arena size and robot sensor and actuator characteristics), evaluates the controller in that environment, and then describes the controller in terms of the features of that environment, the environment descriptor. Our study compares QED to three baseline task-specific and generic behavioral descriptors, in 5 different robot swarm benchmark tasks. For each task, the quality of the evolved archives is assessed by their capability to provide high-performing compensatory behaviors following injection of 250 unique faults to the robots of the swarm. The evolved archives achieve a median 2- to 3-fold reduction in the impact of the faults on the performance of the swarm. A qualitative analysis of evolved archives is done by visualizing the relation between diversity of compensatory behaviors, here called useful behavioral diversity, and fault recovery metrics. The resulting signatures indicate that, due to the diversity of environments inducing useful behavioral diversity, archives evolved by QED provide robot swarm controllers that are capable of recovering from high-impact faults.

关键词： Robot kinematics Robot sensing systems Task analysis Swarm robotics Legged locomotion Actuators Behavioral diversity evolutionary robotics fault recovery quality-diversity algorithms swarm robotics

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Quality-Diversity Meta-Evolution: Customizing Behavior Spaces to a Meta-Objective

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IEEE TRANSACTIONS ON evolutionary COMPUTATION 2022年第5期26卷 1171-1181页

作者： Bossens, David M. Tarapore, Danesh Univ Southampton Sch Elect & Comp Sci Southampton SO17 1BJ Hants England

Quality-diversity (QD) algorithms evolve behaviorally diverse and high-performing solutions. To illuminate the elite solutions for a space of behaviors, QD algorithms require the definition of a suitable behavior space. If the behavior space is high-dimensional, a suitable dimensionality reduction technique is required to maintain a limited number of behavioral niches. While current methodologies for automated behavior spaces focus on changing the geometry of the behavior space or on unsupervised learning of its key features, there remains a need for customizing behavioral diversity to a particular meta-objective specified by the end user. In the newly emerging framework of QD Meta-Evolution, or QD-Meta for short, one evolves a population of QD algorithms, each with different algorithmic and representational characteristics, to optimize the algorithms and their resulting archives to a user-defined meta-objective. Despite promising results compared to traditional QD algorithms, QD-Meta has yet to be compared to state-of-the-art behavior space automation methods, such as centroidal voronoi tessellations multidimensional archive of phenotypic elites (CVT-MAP-Elites) and autonomous robots realizing their abilities (AURORAs). This article performs an empirical study of QD-Meta on function optimization and multilegged robot locomotion benchmarks. Results demonstrate that QD-Meta archives provide improved average performance and faster adaptation to a priori unknown changes to the environment when compared to CVT-MAP-Elites and AURORA. A qualitative analysis shows how the resulting archives are tailored to the meta-objectives provided by the end user.

关键词： Databases Statistics Sociology Robots Magnetosphere Ion radiation effects Genetics evolutionary robotics meta-evolution quality-diversity (QD) algorithms

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The Utility of Evolving Simulated Robot Morphology Increases with Task Complexity for Object Manipulation

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ARTIFICIAL LIFE 2010年第3期16卷 201-223页

作者： Bongard, Josh Univ Vermont Burlington VT 05405 USA

Embodied artificial intelligence argues that the body and brain play equally important roles in the generation of adaptive behavior. An increasingly common approach therefore is to evolve an agent's morphology along with its control in the hope that evolution will find a good coupled system. In order for embodied artificial intelligence to gain credibility within the robotics and cognitive science communities, however, it is necessary to amass evidence not only for how to co-optimize morphology and control of adaptive machines, but why. This work provides two new lines of evidence for why this co- optimization is useful: Here we show that for an object manipulation task in which a simulated robot must accomplish one, two, or three objectives simultaneously, subjugating more aspects of the robot's morphology to selective pressure allows for the evolution of better robots as the number of objectives increases. In addition, for robots that successfully evolved to accomplish all of their objectives, those composed of evolved rather than fixed morphologies generalized better to previously unseen environmental conditions.

关键词： evolutionary robotics embodied artificial intelligence embodied cognition robot shaping multi-objective optimization

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