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IEEE Congress on evolutionary Computation (IEEE CEC)

作者： Phillips, Antin Du Plessis, Mathys C. Nelson Mandela Univ Dept Comp Sci Port Elizabeth South Africa

ISBN: (纸本)9781728183923

This research proposed augmenting evolutionary robotics controllers with their own robotic simulator. The motivation behind this idea is that the controller could use the simulator to approximate the effects of its actions. This would allow the controller to maintain an approximation of the robot's location and orientation. It was hypothesised that this information could enhance a controller's navigation ability. Both regular and augmented controllers were tested and compared on a task that required them to employ adaptive navigation strategies to efficiently solve the task. Augmented controllers were shown to perform better and possess superior behaviours in comparison to regular controllers.

关键词： evolutionary robotics Neural Networks Simulator Neural Networks Proprioception Path Integration Location Perception

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Filling the Reality Gap: Using Obstacles to Promote Robust Gaits in evolutionary robotics

Filling the Reality Gap: Using Obstacles to Promote Robust G...

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IEEE International Conference on Evolvable Systems (ICES)

作者： Glette, Kyrre Johnsen, Andreas Leret Samuelsen, Eivind Univ Oslo Dept Informat N-0316 Oslo Norway

ISBN: (纸本)9781479944798

In evolutionary robotics, which concerns automatic design of robotic systems using evolutionary algorithms, the well-known reality gap phenomenon occurs when transferring results from simulation to real world robots. Several approaches have been proposed to tackle this challenge, such as improving the simulator, avoiding poorly simulated solutions, or promoting robust controllers by introducing noise in the simulation. In this paper we investigate if the addition of a set of small obstacles in the simulated environment can help promote more robust gaits when transferred to a real world robot. In total 80 robot gaits are tested in the real world, evolved using flat and obstacle-seeded ground planes, and using two different scenario difficulties. The results show that in the baseline scenario the proposed obstacle method has little impact on the reality gap of the evolved gaits, whereas there is a significant reduction for the difficult scenario: The average real world performance ratio is 2.3 times higher than the result obtained with the flat plane, and there are no null-performing gaits.

关键词： collision avoidance evolutionary computation mobile robots robust control automatic design evolutionary algorithms evolutionary robotics evolved gaits flat plane imulated environment null-performing gaits obstacle method obstacle-seeded ground planes real world robots reality gap phenomenon robotic systems robust controllers robust gaits simulated solutions simulator Joints Knee Legged locomotion Noise Robustness Servomotors evolutionary algorithm Computer-Aided Design Robust controllers Servo Device Component Biped locomotion Gait Collision avoidance Mobile robots Robust control simulator Noise Joints knee Reality

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Explorations in evolutionary robotics

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Adaptive Behavior 1993年第1期2卷 73-110页

作者： Cliff, Dave Husbands, Phil Harvey, Inman School of Cogmtive and Computing Sciences and Neuroscience IRC School of Biological Sciences University of Sussex Brighton BN1 9QH United Kingdom School of Cognitive and Computing Sciences University of Sussex Brighton BN1 9QH United Kingdom

We discuss the methodological foundations for our work on the development of cognitive architectures, or control systems, for situated autonomous agents. Our focus is the problems of developing sensorimotor control systems for mobile robots, but we also discuss the applicability of our approach to the study of biological systems. We argue that, for agents required to exhibit sophisticated interactions with their environments, complex sensorimotor processing is necessary, and the design, by hand, of control systems capable of such processing is likely to become prohibitively difficult as complexity increases. We propose an automatic design process involving artificial evolution, wherein the basic building blocks used for evolving cognitive architectures are noise-tolerant dynamical neural networks. These networks may be recurrent and should operate in real time. The evolution should be incremental, using an extended and modified version of a genetic algorithm. Practical constraints suggest that initial architecture evaluations should be done largely in simulation. To support our claims and proposals, we summarize results from some preliminary simulation experiments in which visually guided robots are evolved to operate in simple environments. Significantly, our results demonstrate that robust visually guided control systems evolve from evaluation functions that do not explicitly require monitoring visual input. We outline the difficulties involved in continuing with simulations and conclude by describing specialized visuorobotic equipment, designed to eliminate the need for simulated sensors and actuators. © 1993, Sage Publications. All rights reserved.

关键词： autonomous agents evolutionary robotics genetic algorithms SAGA neural networks sensorimotor coordination

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Learning from Evolution: Improving Collective Decision-Making Mechanisms using Insights from evolutionary robotics

Learning from Evolution: Improving Collective Decision-Makin...

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Genetic and evolutionary Computation Conference (GECCO)

作者： Kaiser, Tanja Katharina Univ Technol Nuremberg Nurnberg Germany

ISBN: (纸本)9798400704949

Collective decision-making enables multi-robot systems to act autonomously in real-world environments. Existing collective decision-making mechanisms suffer from the so-called speed versus accuracy trade-off or rely on high complexity, e.g., by including global communication. Recent work has shown that more efficient collective decision-making mechanisms based on artificial neural networks can be generated using methods from evolutionary computation. A major drawback of these decision-making neural networks is their limited interpretability. Analyzing evolved decision-making mechanisms can help us improve the efficiency of hand-coded decision-making mechanisms while maintaining a higher interpretability. In this paper, we analyze evolved collective decision-making mechanisms in detail and hand-code two new decision-making mechanisms based on the insights gained. In benchmark experiments, we show that the newly implemented collective decision-making mechanisms are more efficient than the state-of-the-art collective decision-making mechanisms voter model and majority rule.

关键词： multi-robot systems collective decision-making collective perception evolutionary robotics

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Some Distance Measures for Morphological Diversification in Generative evolutionary robotics 14

Some Distance Measures for Morphological Diversification in ...

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16th Genetic and evolutionary Computation Conference (GECCO)

作者： Samuelsen, Eivind Glette, Kyrre Univ Oslo Inst Informat N-0316 Oslo Norway

ISBN: (纸本)9781450326629

evolutionary robotics often involves optimization in large, complex search spaces, requiring good population diversity. Recently, measures to actively increase diversity or novelty have been employed in order to get sufficient exploration of the search space either as the sole optimization objective or in combination with some performance measurement. When evolving morphology in addition to the control system, it can be difficult to construct a measure that sufficiently captures the qualitative differences between individuals. In this paper we investigate four diversity measures, applied in a set of evolutionary robotics experiments using an indirect encoding for evolving robot morphology. In the experiments we optimize forward locomotion capabilities of symmetrical legged robots in a physics simulation. Two distance measures in Cartesian phenotype feature spaces are compared with two methods operating in the space of possible morphology graphs. These measures are used for computing a diversity objective in a multi-objective evolutionary algorithm, and compared to a control case with no diversity objective. For the given task one of the distance measures shows a clear improvement over the control case in improving the main objectives, while others display better ability to diversify, underlining the difficulty of designing good, general measures of morphological diversity.

关键词： Design experimentation evolutionary robotics indirect encoding multiple solutions / niching

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Developmental analysis in evolutionary robotics

Developmental analysis in evolutionary robotics

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IEEE Mountain Workshop on Adaptive and Learning Systems

作者： Nelson, A. L. Grant, E. Androt LLC POB 44065 Tucson AZ 85733 USA North Carolina State Univ Dept ECE Raleigh NC 27695 USA

ISBN: (纸本)1424401666

This paper presents a developmental analysis of robot controllers created using evolutionary robotics (ER) methods. ER uses artificial evolution to automatically design and synthesize intelligent robot controllers. An aggregate fitness function that injects relatively little a priori task knowledge into the evolving controllers was used. We analyze the course of development of robot controllers evolving to perform a competitive goal-locating task. To sample the course of evolution, controllers were taken from progressively more advanced generations, and were then tested in a novel environment. Developments and changes in the controllers' abilities and competencies were identified and correlated with overall controller fitness. As evolution progressed, it was found that robots evolved more complex high-level behaviors that were not explicitly selected for by the fitness function.

关键词： evolutionary robotics developmental robotics evolutionary neural networks swarm robotics

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EvoRobogami: Co-designing with Humans in evolutionary robotics Experiments

EvoRobogami: Co-designing with Humans in Evolutionary Roboti...

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Genetic and evolutionary Computation Conference (GECCO)

作者： Huang Zonghao Wu, Quinn Howard, David Sung, Cynthia Univ Penn Philadelphia PA 19104 USA CSIRO Brisbane Qld Australia

ISBN: (纸本)9781450392372

We study the effects of injecting human-generated designs into the initial population of an evolutionary robotics experiment, where subsequent population of robots are optimised via a Genetic Algorithm and MAP-Elites. First, human participants interact via a graphical front-end to explore a directly-parameterised legged robot design space and attempt to produce robots via a combination of intuition and trial-and-error that perform well in a range of environments. Environments are generated whose corresponding high-performance robot designs range from intuitive to complex and hard to grasp. Once the human designs have been collected, their impact on the evolutionary process is assessed by replacing a varying number of designs in the initial population with human designs and subsequently running the evolutionary algorithm. Our results suggest that a balance of random and hand-designed initial solutions provides the best performance for the problems considered, and that human designs are most valuable when the problem is intuitive. The influence of human design in an evolutionary algorithm is a highly understudied area, and the insights in this paper may be valuable to the area of AI-based design more generally.

关键词： evolutionary robotics interactive evolution MAP-Elites quality diversity design assist

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Combining Fitness-based Search and User Modeling in evolutionary robotics 13

Combining Fitness-based Search and User Modeling in Evolutio...

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15th Genetic and evolutionary Computation Conference (GECCO)

作者： Bongard, Josh C. Hornby, Gregory S. Univ Vermont Dept Comp Sci Burlington VT 05405 USA Univ Calif Santa Cruz Santa Cruz CA 95064 USA NASA Ames Res Ctr Moffett Field CA 94035 USA

ISBN: (纸本)9781450319638

Methodologies are emerging in many branches of computer science that demonstrate how human users and automated algorithms can collaborate on a problem such that their combined solutions outperform those produced by either humans or algorithms alone. The problem of behavior optimization in robotics seems particularly well-suited for this approach because humans have intuitions about how animals-and thus robots-should and should not behave, and can visually detect non-optimal behaviors that are trapped in local optima. Here we introduce a multiobjective approach in which a surrogate user (which stands in for a human user) deflects search away from local optima and a traditional fitness function eventually leads search toward the global optimum. We show that this approach produces superior solutions for a deceptive robotics problem compared to a similar search method that is guided by just a surrogate user or just a fitness function.

关键词： evolutionary robotics Interactive evolutionary Algorithms evolutionary Algorithms

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On Self-Adaptive Rate Restarts for evolutionary robotics with Real Rotorcraft 17

On Self-Adaptive Rate Restarts for Evolutionary Robotics wit...

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Genetic and evolutionary Computation Conference (GECCO)

作者： Howard, Gerard David CSIRO 1 Technol Court Brisbane Qld 4068 Australia

ISBN: (纸本)9781450349208

Self-adaptive parameters are increasingly used in the field of evolutionary robotics, as they allow key evolutionary rates to vary autonomously in a context-sensitive manner throughout the optimisation process. A significant limitation to self-adaptive mutation is that rates can be set unfavourably, which hinders convergence. Rate restarts are typically employed to remedy this, but thus far have only been applied in evolutionary robotics for mutation-only algorithms. This paper focuses on the level at which evolutionary rate restarts are applied in population-based algorithms with >1 evolutionary operator. After testing on a real hexacopter hovering task, we conclude that individual-level restarting results in higher fitness solutions without fitness stagnation, and population restarts provide a more stable rate evolution. Without restarts, experiments can become stuck in suboptimal controller/rate combinations which can be difficult to escape from.

关键词： evolutionary robotics self-adaptation evolvable hardware differential evolution

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Combining Conflicting Environmental and Task Requirements in evolutionary robotics 9

Combining Conflicting Environmental and Task Requirements in...

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9th IEEE International Conference on Self-Adaptive and Self-Organizing Systems (SASO)

作者： Haasdijk, Evert Vrije Univ Amsterdam Dept Comp Sci Amsterdam Netherlands

ISBN: (纸本)9781467375351

The MONEE framework endows collective adaptive robotic systems with the ability to combine environment- and task-driven selection pressures: it enables distributed online algorithms for learning behaviours that ensure both survival and accomplishment of user-defined tasks. This paper explores the trade-off between these two requirements that evolution must establish when the task is detrimental to survival. To this end, we investigate experiments with populations of 100 simulated robots in a foraging task scenario where successfully collecting resources negatively impacts an individual's remaining lifetime. We find that the population remains effective at the task of collecting pucks even when the negative impact of collecting a puck is as bad as halving the remaining lifetime. A quantitative analysis of the selection pressures reveals that the task-based selection exerts a higher pressure than the environment.

关键词： robots MONEE framework collective adaptive robotic systems conflicting environmental requirement distributed online algorithms environment-driven selection pressure evolutionary robotics foraging task scenario learning behaviours quantitative analysis task requirement task-based selection task-driven selection pressure user-defined tasks Evolution (biology) Genomics Robot sensing systems Sociology Statistics Toxicology Embodied Evolution Environment-driven Evolution evolutionary robotics Manipulator sensing systems Sociology Robot QUANTITATIVE ANALYSIS Toxicology Genomics

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