Revolve: A Versatile Simulator for Online Robot Evolution

Hupkes, Elte; Jelisavcic, Milan; Eiben, A. E.

doi:10.1007/978-3-319-77538-8_46

Cited by 31 publications

(29 citation statements)

References 21 publications

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“…Therefore, we conduct our experiments with the Revolve simulator, cf. Hupkes et al ( 2018 ) 5 . Technically, Revolve is a wrapper around Gazebo, a general-purpose robotic simulator based on the ODE physics engine, that manages insertion, deletion, and production of robots in a simulated environment.…”

Section: Methodsmentioning

confidence: 99%

Lamarckian Evolution of Simulated Modular Robots

et al. 2019

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We study evolutionary robot systems where not only the robot brains but also the robot bodies are evolvable. Such 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 the possibility of bootstrapping infant robot learning through employing Lamarckian inheritance of parental controllers. In our system controllers are encoded by a combination of a morphology dependent component, a Central Pattern Generator (CPG), and a morphology independent part, a Compositional Pattern Producing Network (CPPN). This makes it possible to transfer the CPPN part of controllers between different morphologies and to create a Lamarckian system. We conduct experiments with simulated modular robots whose fitness is determined by the speed of locomotion, establish the benefits of inheriting optimized parental controllers, shed light on the conditions that influence these benefits, and observe that changing the way controllers are evolved also impacts the evolved morphologies.

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Section: Methodsmentioning

confidence: 99%

Lamarckian Evolution of Simulated Modular Robots

et al. 2019

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“…Our experiments were realized using a simulator called Gazebo, interfaced through a robot framework called Revolve ( Hupkes et al, 2018 ). Here, we refer to both morphology (body) and controller (brain) as the phenotype of a robot.…”

Section: System Descriptionmentioning

confidence: 99%

Constrained by Design: Influence of Genetic Encodings on Evolved Traits of Robots

Miras

2021

Front. Robot. AI

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Genetic encodings and their particular properties are known to have a strong influence on the success of evolutionary systems. However, the literature has widely focused on studying the effects that encodings have on performance, i.e., fitness-oriented studies. Notably, this anchoring of the literature to performance is limiting, considering that performance provides bounded information about the behavior of a robot system. In this paper, we investigate how genetic encodings constrain the space of robot phenotypes and robot behavior. In summary, we demonstrate how two generative encodings of different nature lead to very different robots and discuss these differences. Our principal contributions are creating awareness about robot encoding biases, demonstrating how such biases affect evolved morphological, control, and behavioral traits, and finally scrutinizing the trade-offs among different biases.

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“…For every joint in the morphology, there exists a corresponding oscillator neuron in the network, whose activation function is defined by Equation (1), which represents a sine wave defined by amplitude, period, and phase offset parameters. This activation function adjusts the output to fit the range of our servo motors, as proposed in Hupkes et al ( 2018 ).…”

Section: Methodsmentioning

confidence: 99%

“…The first set of experiments used the Baseline encoding, while the second set used the Plasticoding encoding. Our experiments were realized using a simulator called Gazebo, interfaced through a robot framework called Revolve (Hupkes et al, 2018 ). The code needed to reproduce our experiments and analysis in available on GitHub 3 , and the data is available (upon request) in the server ssh.data.vu.nl inside the karinemiras-frontiers2020 directory.…”

Section: Methodsmentioning

confidence: 99%

Environmental Regulation Using Plasticoding for the Evolution of Robots

2020

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Evolutionary robot systems are usually affected by the properties of the environment indirectly through selection. In this paper, we present and investigate a system where the environment also has a direct effect—through regulation. We propose a novel robot encoding method where a genotype encodes multiple possible phenotypes, and the incarnation of a robot depends on the environmental conditions taking place in a determined moment of its life. This means that the morphology, controller, and behavior of a robot can change according to the environment. Importantly, this process of development can happen at any moment of a robot's lifetime, according to its experienced environmental stimuli. We provide an empirical proof-of-concept, and the analysis of the experimental results shows that environmental regulation improves adaptation (task performance) while leading to different evolved morphologies, controllers, and behavior.

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Revolve: A Versatile Simulator for Online Robot Evolution

Cited by 31 publications

References 21 publications

Lamarckian Evolution of Simulated Modular Robots

Lamarckian Evolution of Simulated Modular Robots

Constrained by Design: Influence of Genetic Encodings on Evolved Traits of Robots

Environmental Regulation Using Plasticoding for the Evolution of Robots

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