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ADVANCED INTELLIGENT SYSTEMS 2019年第6期1卷

作者： Jones, Simon Winfield, Alan F. Hauert, Sabine Studley, Matthew Univ Bristol Bristol Robot Lab Senate HouseTyndall Ave Bristol BS8 1TH Avon England Univ West England Bristol Robot Lab Bristol BS16 1QY Avon England

Designing the individual robot rules that give rise to desired emergent swarm behaviors is difficult. The common method of running evolutionary algorithms off-line to automatically discover controllers in simulation suffers from two disadvantages: the generation of controllers is not situated in the swarm and so cannot be performed in the wild, and the evolved controllers are often opaque and hard to understand. A swarm of robots with considerable on-board processing power is used to move the evolutionary process into the swarm, providing a potential route to continuously generating swarm behaviors adapted to the environments and tasks at hand. By making the evolved controllers human-understandable using behavior trees, the controllers can be queried, explained, and even improved by a human user. A swarm system capable of evolving and executing fit controllers entirely onboard physical robots in less than 15min is demonstrated. One of the evolved controllers is then analyzed to explain its functionality. With the insights gained, a significant performance improvement in the evolved controller is engineered.

关键词： evolutionary robotics swarm robotics behavior trees explainability

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Achieving Connectivity Between Wide Areas Through Self-Organising Robot Swarms Using Embodied Evolution 8

Achieving Connectivity Between Wide Areas Through Self-Organ...

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8th IEEE Symposium Series on Computational Intelligence (IEEE SSCI)

作者： Hansen, Erik Aaron Nichele, Stefano Yazidi, Anis Haugerud, Harek Mofrad, Asieh Abolpour Alcocer, Alex Oslo Metropolitan Univ Dept Comp Sci Oslo Norway Oslo Metropolitan Univ Dept Mech Elect & Chem Engn Oslo Norway

ISBN: (纸本)9781538692769

Abruptions to the communication infrastructure might occur occasionally, where manual intervention by dedicated personnel is needed to fix the interruptions, restoring communication abilities. However, sometimes this can be dangerous to the personnel carrying out the task, which can be the case in environmental disasters like earthquakes or toxic spills or in the occurrence of tire. Therefore, personal risks can he minimised if autonomous robots are deployed to achieve the same outcome: to establish a communication link between two previously connected but distant sites. In this paper, we investigate the deployment of mobile ad hoc robots which relay traffic between two sites. In order to get the robots to locate themselves appropriately, we take inspiration from self-organisation and emergence in artificial life, where a common overall goal may be achieved if the correct local rules on the agents in the system are invoked. We integrate the aspect of connectivity between two sites into the multirobot simulation platform known as JBotEvolver. The robot swarm is composed of Thymio II robots. In addition, we compare three heuristics, of which one uses neuroevolution (evolution of neural networks) to show how self-organisation and embodied evolution can be used within the integration. Our use of embodiment in robotic controllers shows promising results and provides solid knowledge and guidelines for further investigations.

关键词： Swarm robotics evolutionary robotics Embodied Evolution Internet of Things

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Evolving Robust Solutions for Stochastically Varying Problems

Evolving Robust Solutions for Stochastically Varying Problem...

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IEEE Congress on evolutionary Computation (IEEE CEC) as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Carvalho, Jonata Tyska Milano, Nicola Nolfi, Stefano Fundacao Univ Fed Rio Grande Ctr Computat Sci C3 Av ItaliaKm 8 Rio Grande Brazil CNR Natl Res Council Inst Cognit Sci & Technol Rome Italy CNR Inst Cognit Sci & Technol Rome Italy

ISBN: (纸本)9781509060177

We demonstrate how evaluating candidate solutions in a limited number of stochastically varying conditions that vary over generations at a moderate rate is an effective method for developing high quality robust solutions. Indeed, agents evolved with this method for the ability to solve an extended version of the double-pole balancing problem, in which the initial state of the agents and the characteristics of the environment in which the agents are situated vary, show the ability to solve the problem in a wide variety of environmental circumstances and for prolonged periods of time without the need to readapt. The combinatorial explosion of possible environmental conditions does not prevent the evolution of robust solutions. Indeed, exposing evolving agents to a limited number of different environmental conditions that vary over generations is sufficient and leads to better results with respect to control experiments in which the number of experienced environmental conditions is greater. Interestingly the exposure to environmental variations promotes the evolution of convergent strategies in which the agents act so to exhibit the required functionality and so to reduce the complexity of the control problem.

关键词： evolutionary robotics robustness environmental variation adaptive behaviour

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Effects of Selection Preferences on Evolved Robot Morphologies and Behaviors

Effects of Selection Preferences on Evolved Robot Morphologi...

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Conference on Artificial Life (ALIFE)

作者： Miras, Karine Haasdijk, Evert Glette, Kyrre Eiben, A. E. Vrije Univ Amsterdam Amsterdam Netherlands Univ Oslo Oslo Norway

This paper investigates the evolution of modular robots using different selection preferences (i.e., fitness functions), aiming at novelty, speed of locomotion, number of limbs, and combinations of these. The outcomes are analyzed from different perspectives: sampling of the search space, evolved morphologies, and evolved behaviors. This results in a wealth of findings, including a surprise about the number of sampled regions of the search space and the effect of different fitness functions on the evolved morphologies.

关键词： evolutionary robotics morphology evolution generative encoding novelty search search space

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Search Space Analysis of Evolvable Robot Morphologies 21st

Search Space Analysis of Evolvable Robot Morphologies

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21st International Conference on the Applications of evolutionary Computation (EvoApplications)

作者： Miras, Karine Haasdijk, Evert Glette, Kyrre Eiben, A. E. Vrije Univ Amsterdam Amsterdam Netherlands Univ Oslo Oslo Norway

ISBN: (纸本)9783319775388

We present a study on morphological traits of evolved modular robots. We note that the evolutionary search space -the set of obtainable morphologies- depends on the given representation and reproduction operators and we propose a framework to assess morphological traits in this search space regardless of a specific environment and/or task. To this end, we present eight quantifiable morphological descriptors and a generic novelty search algorithm to produce a diverse set of morphologies for any given representation. With this machinery, we perform a comparison between a direct encoding and a generative encoding. The results demonstrate that our framework permits to find a very diverse set of bodies, allowing a morphological diversity investigation. Furthermore, the analysis showed that despite the high levels of diversity, a bias to certain traits in the population was detected. Surprisingly, the two encoding methods showed no significant difference in the diversity levels of the evolved morphologies or their morphological traits.

关键词： Modular robots evolutionary robotics Morphology Generative encoding Novelty search

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Evolution of a Functionally Diverse Swarm via a Novel Decentralised Quality-Diversity Algorithm 18

Evolution of a Functionally Diverse Swarm via a Novel Decent...

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Genetic and evolutionary Computation Conference (GECCO)

作者： Hart, Emma Steyven, Andreas S. W. Paechter, Ben Edinburgh Napier Univ Edinburgh Midlothian Scotland

ISBN: (纸本)9781450356183

The presence of functional diversity within a group has been demonstrated to lead to greater robustness, higher performance and increased problem-solving ability in a broad range of studies that includes insect groups, human groups and swarm robotics. Evolving group diversity however has proved challenging within evolutionary robotics, requiring reproductive isolation and careful attention to population size and selection mechanisms. To tackle this issue, we introduce a novel, decentralised, variant of the MAP-Elites illumination algorithm which is hybridised with a well-known distributed evolutionary algorithm (mEDEA). The algorithm simultaneously evolves multiple diverse behaviours for multiple robots, with respect to a simple token-gathering task. Each robot in the swarm maintains a local archive defined by two pre-specified functional traits which is shared with robots it come into contact with. We investigate four different strategies for sharing, exploiting and combining local archives and compare results to mEDEA. Experimental results show that in contrast to previous claims, it is possible to evolve a functionally diverse swarm without geographical isolation, and that the new method outperforms mEDEA in terms of the diversity, coverage and precision of the evolved swarm.

关键词： Functional Diversity evolutionary robotics Map-Elites

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Real-World Evolution Adapts Robot Morphology and Control to Hardware Limitations 18

Real-World Evolution Adapts Robot Morphology and Control to ...

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Genetic and evolutionary Computation Conference (GECCO)

作者： Nygaard, Tonnes F. Martin, Charles P. Samuelsen, Eivind Torresen, Jim Glette, Kyrre Univ Oslo Oslo Norway

ISBN: (纸本)9781450356183

For robots to handle the numerous factors that can afect them in the real world, they must adapt to changes and unexpected events. evolutionary robotics tries to solve some of these issues by automatically optimizing a robot for a speciic environment. Most of the research in this ield, however, uses simpliied representations of the robotic system in software simulations. The large gap between performance in simulation and the real world makes it challenging to transfer the resulting robots to the real world. In this paper, we apply real world multi-objective evolutionary optimization to optimize both control and morphology of a four-legged mammal-inspired robot. We change the supply voltage of the system, reducing the available torque and speed of all joints, and study how this afects both the itness, as well as the morphology and control of the solutions. In addition to demonstrating that this realworld evolutionary scheme for morphology and control is indeed feasible with relatively few evaluations, we show that evolution under the diferent hardware limitations results in comparable performance for low and moderate speeds, and that the search achieves this by adapting both the control and the morphology of the robot.

关键词： evolutionary robotics Evolution of Morpholgy Real-world Evolution Evolvable Hardware

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Directed Locomotion for Modular Robots with Evolvable Morphologies 15th

Directed Locomotion for Modular Robots with Evolvable Morpho...

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15th International Conference on Parallel Problem Solving from Nature (PPSN)

作者： Lan, Gongjin Jelisavcic, Milan Roijers, Diederik M. Haasdijk, Evert Eiben, A. E. Vrije Univ Amsterdam Dept Comp Sci Amsterdam Netherlands

ISBN: (纸本)9783319992532;9783319992525

Morphologically evolving robot systems need to include a learning period right after 'birth' to acquire a controller that fits the newly created body. In this paper, we investigate learning one skill in particular: walking in a given direction. To this end, we apply the Hyper-NEAT algorithm guided by a fitness function that balances the distance travelled in a direction and the deviation between the desired and the actually travelled directions. We validate this method on a variety of modular robots with different shapes and sizes and observe that the best controllers produce trajectories that accurately follow the correct direction and reach a considerable distance in the given test interval.

关键词： evolutionary robotics Evolvable morphologies Modular robots Gait learning Directed locomotion

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Fusing Novelty and Surprise for Evolving Robot Morphologies 18

Fusing Novelty and Surprise for Evolving Robot Morphologies

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Genetic and evolutionary Computation Conference (GECCO)

作者： Gravina, Daniele Liapis, Antonios Yannakakis, Georgios N. Univ Malta Inst Digital Games Msida Malta

ISBN: (纸本)9781450356183

Traditional evolutionary algorithms tend to converge to a single good solution, which can limit their chance of discovering more diverse and creative outcomes. Divergent search, on the other hand, aims to counter convergence to local optima by avoiding selection pressure towards the objective. Forms of divergent search such as novelty or surprise search have proven to be beneficial for both the efficiency and the variety of the solutions obtained in deceptive tasks. Importantly for this paper, early results in maze navigation have shown that combining novelty and surprise search yields an even more effective search strategy due to their orthogonal nature. Motivated by the largely unexplored potential of coupling novelty and surprise as a search strategy, in this paper we investigate how fusing the two can affect the evolution of soft robot morphologies. We test the capacity of the combined search strategy against objective, novelty, and surprise search, by comparing their efficiency and robustness, and the variety of robots they evolve. Our key results demonstrate that novelty-surprise search is generally more efficient and robust across eight different resolutions. Further, surprise search explores the space of robot morphologies more broadly than any other algorithm examined.

关键词： Surprise search novelty search divergent search deception evolutionary robotics soft robots CPPN artificial life

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A Robot to Shape your Natural Plant: The Machine Learning Approach to Model and Control Bio-Hybrid Systems 18

A Robot to Shape your Natural Plant: The Machine Learning Ap...

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Genetic and evolutionary Computation Conference (GECCO)

作者： Wahby, Mostafa Heinrich, Mary Katherine Hofstadler, Daniel Nicolas Zahadat, Payam Risi, Sebastian Ayres, Phil Schmickl, Thomas Hamann, Heiko Univ Lubeck Inst Comp Engn Lubeck Germany Royal Danish Acad Ctr IT & Architecture Copenhagen Denmark Karl Franzens Univ Graz Artificial Life Lab Graz Austria ITU Copenhagen Robot Evolut & Art Lab Copenhagen Denmark

ISBN: (纸本)9781450356183

Bio-hybrid systems-close couplings of natural organisms with technology-are high potential and still underexplored. In existing work, robots have mostly influenced group behaviors of animals. We explore the possibilities of mixing robots with natural plants, merging useful attributes. Significant synergies arise by combining the plants' ability to efficiently produce shaped material and the robots' ability to extend sensing and decision-making behaviors. However, programming robots to control plant motion and shape requires good knowledge of complex plant behaviors. Therefore, we use machine learning to create a holistic plant model and evolve robot controllers. As a benchmark task we choose obstacle avoidance. We use computer vision to construct a model of plant stem stiffening and motion dynamics by training an LSTM network. The LSTM network acts as a forward model predicting change in the plant, driving the evolution of neural network robot controllers. The evolved controllers augment the plants' natural light-finding and tissue-stiffening behaviors to avoid obstacles and grow desired shapes. We successfully verify the robot controllers and bio-hybrid behavior in reality, with a physical setup and actual plants.

关键词： bio-hybrid system natural plants decision making evolutionary robotics genetic algorithms neural networks

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