Scaffolding-initially simplifying the task environment of autonomous robots-has been shown to increase the probability of evolving robots capable of performing in more complex task environments. Recently, it has been ...
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ISBN:
(纸本)9781450305570
Scaffolding-initially simplifying the task environment of autonomous robots-has been shown to increase the probability of evolving robots capable of performing in more complex task environments. Recently, it has been shown that changes to the body of a robot may also scaffold the evolution of non trivial behavior. This raises the question of whether two different kinds of scaffolding (environmental and morphological) synergize with one another when combined. Here it is shown that, for legged robots evolved to perform phototaxis, synergy can be achieved, but only if morphological and environmental scaffolding are combined in a particular way: The robots must first undergo morphological scaffolding, followed by environmental scaffolding. This suggests that additional kinds of scaffolding may create additional synergies that lead to the evolution of increasingly complex robot behaviors.
In order to evolve large robot controllers for increasingly complex tasks, fully connected neural networks are not feasible. However, manually designing sparse neural connectivity is not intuitive, and thus should be ...
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ISBN:
(纸本)9781450305570
In order to evolve large robot controllers for increasingly complex tasks, fully connected neural networks are not feasible. However, manually designing sparse neural connectivity is not intuitive, and thus should be placed under evolutionary control. Here I show how spontaneous structural modularity can arise in the connectivity of evolved robot controllers if the controllers are boolean networks, and are selected to converge on point attractors that correspond to successful robot behaviors.
Exploration and exploitation are two complementary aspects of evolutionary Algorithms. Exploration, in particular, is promoted by specific diversity keeping mechanisms generally relying on the genotype or on the fitne...
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ISBN:
(纸本)9781450305570
Exploration and exploitation are two complementary aspects of evolutionary Algorithms. Exploration, in particular, is promoted by specific diversity keeping mechanisms generally relying on the genotype or on the fitness value. Recent works suggest that, in the case of evolutionary robotics or more generally behavioral system evolution, promoting exploration directly in the behavioral space is of critical importance. In this work an exploration indicator is proposed, based on the sparseness of the population in the behavioral space. This exploration measure is used on two challenging neuro-evolution experiments and validated by showing the dependence of the fitness at the end of the run on the exploration measure during the very first generations. Such a prediction ability could be used to design parameter settings algorithms or selection algorithms dedicated to the evolution of behavioral systems. Several other potential uses of this measure are also proposed and discussed.
This paper extends prior work using Compositional Pattern Producing Networks (CPPNs) as a generative encoding for the purpose of simultaneously evolving robot morphology and control. A method is presented for translat...
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ISBN:
(纸本)9781450305570
This paper extends prior work using Compositional Pattern Producing Networks (CPPNs) as a generative encoding for the purpose of simultaneously evolving robot morphology and control. A method is presented for translating CPPNs into complete robots including their physical topologies, sensor placements, and embedded, closed-loop, neural network control policies. It is shown that this method can evolve robots for a given task. Additionally it is demonstrated how the performance of evolved robots can be significantly improved by allowing recurrent connections with in the underlying CPPNs. The resulting robots are analyzed in the hopes of answering why these recurrent connections prove to be so beneficial in this domain. Several hypotheses are discussed, some of which are refuted from the available data while others will require further examination.
In this paper, a theoretical and experimental study of the influence of environments on the selection process in evolutionary swarm robotics is conducted. The theoretical selection model is based on Markov chains. It ...
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ISBN:
(纸本)9789898425836
In this paper, a theoretical and experimental study of the influence of environments on the selection process in evolutionary swarm robotics is conducted. The theoretical selection model is based on Markov chains. It is proposed to predict the success rate of evolutionary runs which are based on a selection mechanism depending on implicit environmental properties as well as an explicit fitness function. In the experiments, the interaction of explicit and implicit selection is studied and a comparison with the model prediction is performed. The results indicate that the model prediction is accurate for the studied cases.
In this paper we report our experiments with the e-puck robots for developing a communication system using evolutionary robotics. In order to do the latter we follow the evolutionary approach by using Neural Networks ...
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ISBN:
(纸本)9783642253232;9783642253249
In this paper we report our experiments with the e-puck robots for developing a communication system using evolutionary robotics. In order to do the latter we follow the evolutionary approach by using Neural Networks and Genetic Algorithms. The robots develop a communication scheme for solving tasks like: locating food areas, avoiding obstacles, approaching light sources and locating sound-sources (other robots emitting sounds). Evorobot* and Webots simulators are used as tools for computing the evolutionary process and optimization of the weights of neural controllers. As a consequence, two different kinds of neural controllers emerge. On one hand, one controller is used for robot movement;on the other hand the second controller process sound signals.
Anytime Learning with Fitness Biasing has been shown in previous works to be an effective tool for evolving hexapod gaits. In this paper, we present the use of Anytime Learning with Fitness Biasing to evolve the contr...
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ISBN:
(纸本)9781457706530
Anytime Learning with Fitness Biasing has been shown in previous works to be an effective tool for evolving hexapod gaits. In this paper, we present the use of Anytime Learning with Fitness Biasing to evolve the controller for a robot learning the box pushing task. The robot that was built for this task, was measured to create an accurate model. The model was used in simulation to test the effectiveness of Anytime Learning with Fitness Biasing for the box pushing task. This work is the first step in new research where an automated system to test the viability of Fitness Biasing will be created, as well as the first application of Fitness Biasing to a high level task such as box pushing.
It is a difficult task to generate optimal walking gaits for mobile legged robots. Generating and coordinating an optimal gait involves continually repeating a series of actions in order to create a sustained movement...
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ISBN:
(纸本)9781457706530
It is a difficult task to generate optimal walking gaits for mobile legged robots. Generating and coordinating an optimal gait involves continually repeating a series of actions in order to create a sustained movement. In this work, we present the use of a Cyclic Genetic Algorithm (CGA) to learn near optimal gaits for an actual quadruped servo-robot with three degrees of movement per leg. This robot was used to create a simulation model of the movement and states of the robot which included the robot's unique features and capabilities. The CGA used this model to learn gaits that were optimized for this particular robot. Tests done in simulation show the success of the CGA in evolving gait control programs and tests on robot show that these control programs produce reasonable gaits.
Generating walking gaits for legged robots is a challenging task. Gait generation with proper leg coordination involves a series of actions that are continually repeated to create sustained movement. In this paper we ...
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ISBN:
(纸本)9781424478354
Generating walking gaits for legged robots is a challenging task. Gait generation with proper leg coordination involves a series of actions that are continually repeated to create sustained movement. In this paper we present the use of a Cyclic Genetic Algorithm (CGA) to learn gaits for a quadruped servo-robot with three degrees of movement per leg. An actual robot was used to generate a simulation model of the movement and states of the robot. The CGA used the robot's unique features and capabilities to develop gaits specific for that particular robot. Tests done in simulation show the success of the CGA in evolving a reasonable control program and preliminary tests on the robot show that the resultant control program produces a suitable gait.
evolutionary robotics simulations can serve as a tool to clarify counterintuitive or dynamically complex aspects of sensorimotor behaviour. We present a series of simulations that has been conducted in order to aid th...
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ISBN:
(纸本)9783540749127
evolutionary robotics simulations can serve as a tool to clarify counterintuitive or dynamically complex aspects of sensorimotor behaviour. We present a series of simulations that has been conducted in order to aid the interpretation of ambiguous empirical data on human adaptation to delayed tactile feedback. Agents have been evolved to catch objects falling at different velocities to investigate the behavioural impact that lengthening or shortening of sensory delays has on the strategies evolved. A detailed analysis of the evolved model agents leads to a number of hypotheses for the quantification of the existing data, as well as to ideas for possible further empirical experiments. This study confirms the utility of evolutionary robotics simulation in this kind of interdisciplinary endeavour.
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