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A Hormone-Based Controller for Evaluation-Minimal Evolution in Decentrally Controlled Systems

ARTIFICIAL LIFE 2012年第2期18卷 165-198页

作者： Hamann, Heiko Schmickl, Thomas Crailsheim, Karl Karl Franzens Univ Graz Dept Zool Artificial Life Lab A-8010 Graz Austria

One of the main challenges in automatic controller synthesis is to develop methods that can successfully be applied Lot complex tasks. The difficulty is increased even more in the case of settings with multiple interacting agents. We apply the artificial homeostatic hormone system (AHHS) approach, which is inspired by the signaling network of unicellular organisms, to control a system of several independently acting agents decentrally. The approach is designed for evaluation-minimal, artificial evolution in order to be applicable to complex modular robotics scenarios. The performance of AHHS controllers is compared with ncuroevolution of augmenting topologies (NEAT) in the coupled inverted pendulums benchmark. AHHS controllers are found to be better for multimodular settings. We analyze the evolved controllers with regard to the usage of sensory inputs and the emerging oscillations, and we give a nonlinear dynamics interpretation. The generalization of evolved controllers to initial conditions far from the original conditions is investigated and found to be good. Similarly, the performance of controllers scales well even with module numbers different from the original domain the controller was evolved for. Two reference implementations of a similar controller approach are reported and shown to have shortcomings. We discuss the related work and conclude by summarizing the main contributions of our work.

关键词： Controller synthesis hormone-endocrine system decentral control evolutionary robotics inspiration by signaling networks of unicellular organisms

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Genetic Team Composition and Level of Selection in the Evolution of Cooperation

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IEEE TRANSACTIONS ON evolutionary COMPUTATION 2009年第3期13卷 648-660页

作者： Waibel, Markus Keller, Laurent Floreano, Dario Ecole Polytech Fed Lausanne Sch Engn Lab Intelligent Syst Lausanne Switzerland Univ Lausanne Dept Ecol & Evolut Biophore CH-1015 Lausanne Switzerland

In cooperative multiagent systems, agents interact to solve tasks. Global dynamics of multiagent teams result from local agent interactions, and are complex and difficult to predict. evolutionary computation has proven a promising approach to the design of such teams. The majority of current studies use teams composed of agents with identical control rules ("genetically homogeneous teams") and select behavior at the team level ("team-level selection"). Here we extend current approaches to include four combinations of genetic team composition and level of selection. We compare the performance of genetically homogeneous teams evolved with individual-level selection, genetically homogeneous teams evolved with team-level selection, genetically heterogeneous teams evolved with individual-level selection, and genetically heterogeneous teams evolved with team-level selection. We use a simulated foraging task to show that the optimal combination depends on the amount of cooperation required by the task. Accordingly, we distinguish between three types of cooperative tasks and suggest guidelines for the optimal choice of genetic team composition and level of selection.

关键词： Altruism artificial evolution cooperation evolutionary robotics fitness allocation multiagent systems (MAS) team composition

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Biped locomotion control with evolved adaptive center-crossing continuous time recurrent neural networks

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NEUROCOMPUTING 2012年 86卷 86-96页

作者： Santos, Jose Campo, Angel Univ A Coruna Dept Comp Sci Coruna 15071 A Spain

We used center-crossing continuous time recurrent neural networks as central pattern generator controllers in biped robots, together with an adaptive methodology to improve the ability of the recurrent neural networks to produce rhythmic activation behaviors. The parameters of the recurrent networks are adapted or modified in run-time to reach the center-crossing condition, so the nodes get close to the most sensitive region to their input. This facilitates the evolution of the networks that act as central pattern generators to control biped structures. The robustness of the adaptive networks to produce rhythmic activation patterns was checked as well as the improvements and possibilities this adaptation may add. (C) 2012 Elsevier B.V. All rights reserved.

关键词： Continuous time recurrent neural networks Genetic algorithms evolutionary robotics

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Challenges in cooperative coevolution of physically heterogeneous robot teams

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NATURAL COMPUTING 2019年第1期18卷 29-46页

作者： Gomes, Jorge Mariano, Pedro Christensen, Anders Lyhne BioMachines Lab Lisbon Portugal Inst Telecomunicacoes Lisbon Portugal Univ Lisbon Fac Ciencias BioISI Lisbon Portugal IUL ISCTE Lisbon Portugal

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.

关键词： evolutionary robotics Cooperative coevolution Multirobot systems Heterogeneous systems Premature convergence Artificial neural networks

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Evolving Robotic Hand Morphology Through Grasping and Learning

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IEEE robotics AND AUTOMATION LETTERS 2024年第10期9卷 8475-8482页

作者： Yang, Bangchu Jiang, Li Wu, Wenhao Zhen, Ruichen Harbin Inst Technol State Key Lab Robot & Syst Harbin 150080 Peoples R China

Creatures can co-evolve their biological structures and behaviors under environmental pressures. Leveraging biomimetic evolution algorithms (referred to as co-design or co-optimization), a diverse range of robots with environmental adaptation has been generated. However, implementing these evolutionary methods or results in real-world robots, especially in the case of robotic hands, was not easy. In this context, this work presents a comprehensive self-optimization scheme for robotic hands that encompasses both software and hardware components. This scheme enables robots to autonomously refine their morphology through the integration of hardware gradients and reinforcement learning within parallel environments, thereby enhancing their adaptability to a variety of grasping tasks. For the hardware aspect, we developed a reconfigurable hand prototype with 37 variable hardware parameters (i.e., joint stiffness, the length of phalanges, finger location, and palm curvature) adjusted by mechanical components. Leveraging the adjustable hardware and 20 motors, this hand achieves full actuation and can dynamically adjust its morphology. The training results indicate that the fitness score of the self-optimizing hand exceeds that of original designs in this instance. The hardware parameters can be further fine-tuned in response to task variations. Moreover, the evolved hardware parameters are transferred to a real-world reconfigurable hand, demonstrating its grasping and adaptivity capabilities.

关键词： Hardware Grasping Robots Morphology Optimization Stability analysis Task analysis evolutionary robotics deep learning in grasping and manipulation methods and tools for robot system design hardware gradient reconfigurable hand

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Power and limits of reactive agents

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NEUROCOMPUTING 2002年第1-4期42卷 119-145页

作者： Nolfi, S CNR Inst Psychol I-00137 Rome Italy

This paper shows how reactive agents can solve complex tasks without requiring any internal state and demonstrates that this is due to their ability to coordinate perception and action. By acting (i.e., by modifying their position with respect to the external environment and/or the external environment itself), agents partially determine the sensory patterns they receive from the environment. Agents can take advantage of this ability to: (1) select sensory patterns that are not affected by the aliasing problem and avoiding:those that are;(2) select sensory patterns in which groups of patterns requiring different answers do not strongly overlap;(3) exploit the fact that, given a certain behavior, sensory states might indirectly encode information about useful features of the environment;(4) exploit emergent behaviors resulting from a sequence of sensory-motor loops and from the interaction between the robot and the environment. The relation between pure reactive agents and pure representational agents is discussed and it is argued that a large variety of intermediate cases between these two extremes exists. In particular, attention is given to the case of agents that encode in their internal states what they did in the previous portion of their lifetime which, given a certain behavior, might indirectly encode information about the external environment. (C) 2002 Elsevier Science B.V. All rights reserved.

关键词： sensory-motor coordination embodied intelligence reactive systems internal representations evolutionary robotics

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Towards group transport by swarms of robots

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INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION 2009年第1-2期1卷 1-13页

作者： Gross, Roderich Dorigo, Marco Ecole Polytech Fed Lausanne Lab Syst Robot EPFL STI IMT LSRO Stn 9 CH-1015 Lausanne Switzerland Univ Libre Bruxelles IRIDIA CoDE B-1050 Brussels Belgium

We examine the ability of a swarm robotic system to transport cooperatively objects of different shapes and sizes. We simulate a group of autonomous mobile robots that can physically connect to each other and to the transported object. Controllers - artificial neural networks - are synthesised by an evolutionary algorithm. They are trained to let the robots self-assemble, that is, organise into collective physical structures and transport the object towards a target location. We quantify the performance and the behaviour of the group. We show that the group can cope fairly well with objects of different geometries as well as with sudden changes in the target location. Moreover, we show that larger groups, which are made of up to 16 robots, make possible the transport of heavier objects. Finally, we discuss the limitations of the system in terms of task complexity, scalability and fault tolerance and point out potential directions for future research.

关键词： artificial neural networks bio-inspired computation collective structures cooperation cooperative transport evolutionary robotics foraging group transport modular robots object manipulation reconfigurable robots self-assembling robots self-assembly swarm intelligence swarm robotics

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Evolution of Joint-Level Control for Quadrupedal Locomotion

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ARTIFICIAL LIFE 2017年第1期23卷 58-79页

作者： Moore, Jared M. McKinley, Philip K. Grand Valley State Univ Sch Comp & Informat Syst Allendale MI 49401 USA Michigan State Univ Dept Comp Sci & Engn 428 South Shaw LaneRoom 3115 E Lansing MI 48824 USA

We investigate a hierarchical approach to robot control inspired by joint-level control in animals. The method combines a high-level controller, consisting of an artificial neural network (ANN), with joint-level controllers based on digital muscles. In the digital muscle model (DMM), morphological and control aspects of joints evolve concurrently, emulating the musculoskeletal system of natural organisms. We introduce and compare different approaches for connecting outputs of the ANN to DMM-based joints. We also compare the performance of evolved animats with ANN-DMM controllers with those governed by only high-level (ANN-only) and low-level (DMM-only) controllers. These results show that DMM-based systems outperform their ANN-only counterparts while also exhibiting less complex ANNs in terms of the number of connections and neurons. The main contribution of this work is to explore the evolution of artificial systems where, as in natural organisms, some aspects of control are realized at the joint level.

关键词： evolutionary robotics joint-level control artificial neural network morphology digital muscle

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Evolving an action-based mechanism for the interpretation of geometrical clues during robot navigation

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CONNECTION SCIENCE 2004年第4期16卷 267-281页

作者： Miglino, O Walker, R Seconda Univ Napoli Dipartimento Psicol I-81100 Caserta Italy CNR Inst Cognit Sci & Technol Rome Italy

Kamil and Jones have shown that Clark's nutcrackers (a species of crow) can exploit abstract geometrical relationships between landmarks to reach a target location. This has been interpreted as evidence for the existence of 'cognitive maps'. In the research reported here, we replicate the behaviour observed by Kamil and Jones, using methods from evolutionary robotics. A genetic algorithm is used to train a software model of the Khepera miniature mobile robot, controlled by an artificial neural network. The architecture of the network precludes the existence of cognitive maps. The robots display a range of different behaviours. We demonstrate that these depend on a process of 'active perception' and analyse the underlying computational mechanisms. On the basis of our results, we suggest that animals in the wild may indeed use active perception as a tool to exploit geometrical relationships between landmarks and that such a mechanism might complement other mechanisms, including cognitive maps.

关键词： evolutionary robotics cognitive maps embodied cognition situated models animal navigation

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Reality-Assisted Evolution of Soft Robots through Large-Scale Physical Experimentation: A Review

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ARTIFICIAL LIFE 2021年第4期26卷 484-506页

作者： Howison, Toby Hauser, Simon Hughes, Josie Iida, Fumiya Univ Cambridge Bioinspired Robot Lab Cambridge England

We introduce the framework of reality-assisted evolution to summarize a growing trend towards combining model-based and model-free approaches to improve the design of physically embodied soft robots. In silico, data-driven models build, adapt, and improve representations of the target system using real-world experimental data. By simulating huge numbers of virtual robots using these data-driven models, optimization algorithms can illuminate multiple design candidates for transference to the real world. In reality, large-scale physical experimentation facilitates the fabrication, testing, and analysis of multiple candidate designs. Automated assembly and reconfigurable modular systems enable significantly higher numbers of real-world design evaluations than previously possible. Large volumes of ground-truth data gathered via physical experimentation can be returned to the virtual environment to improve data-driven models and guide optimization. Grounding the design process in physical experimentation ensures that the complexity of virtual robot designs does not outpace the model limitations or available fabrication technologies. We outline key developments in the design of physically embodied soft robots in the framework of reality-assisted evolution.

关键词： Soft robotics embodied evolution evolutionary robotics design optimization

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