Evolution and Morphogenesis of Simulated Modular Robots: A Comparison Between a Direct and Generative Encoding

Veenstra, Frank; Faina, Andres; Risi, Sebastian; Støy, Kasper

doi:10.1007/978-3-319-55849-3_56

Cited by 28 publications

(25 citation statements)

References 29 publications

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“…Direct and indirect encoding have both previously been successfully used to evolve robots [4,29]; here we use a direct encoding of the robots morphology as it allows us to easily inject domain knowledge and constraints whilst allowing the exploration of a reasonably diverse set of morphologies. Constraints are as follows: maximum weight 60kg, forward speed >1m/s, minimum height 2m, payload 10kg (battery, sensors, etc.).…”

Section: Methodsmentioning

confidence: 99%

Diversity-based design assist for large legged robots

Howard

Löwe

Wade

2020

Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion

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We combine MAP-Elites and highly parallelisable simulation to explore the design space of a class of large legged robots, which stand at around 2m tall and whose design and construction is not wellstudied. The simulation is modified to account for factors such as motor torque and weight, and presents a reasonable fidelity search space. A novel robot encoding allows for bio-inspired features such as legs scaling along the length of the body. The impact of three possible control generation schemes are assessed in the context of body-brain co-evolution, showing that even constrained problems benefit strongly from coupling-promoting mechanisms. A two stage process in implemented. In the first stage, a library of possible robots is generated, treating user requirements as constraints. In the second stage, the most promising robot niches are analysed and a suite of human-understandable design rules generated related to the values of their feature variables. These rules, together with the library, are then ready to be used by a (human) robot designer as a Design Assist tool. CCS CONCEPTS • Computer systems organization → Evolutionary robotics; • Theory of computation → Evolutionary algorithms; • Applied computing → Computer-aided design;

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Section: Methodsmentioning

confidence: 99%

Diversity-based design assist for large legged robots

Howard

Löwe

Wade

2020

Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion

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“…Generative Encoding: Our generative encoding represents the genotype of a robot with a Lindenmayer-System (L-System) [9,13], which is a grammatical parallel rewriting system. The grammar of an L-System is defined as a tuple G = (V, w, P ), where -V , the alphabet, is a set of symbols containing replaceable and non-replaceable elements w, the axiom, is a symbol from which the system starts -P is a set of production rules for the replaceable symbols In our design, the symbols of the grammar represent the modules of a robotic body and the commands to assemble them together.…”

Section: Encodingsmentioning

confidence: 99%

“…Evolutionary Robotics (ER) [1][2][3][4] is a field that "aims to apply evolutionary computation techniques to evolve the overall design or controllers, or both, for real and simulated autonomous robots" [3]. Traditionally, the emphasis lies on evolving controllers for fixed robot bodies, but there is a growing interest in evolving the morphologies as well [5][6][7][8][9]. For instance, a generic architecture for a system of embodied on-line evolution of robots in real time and real space was proposed in [10].…”

Section: Introductionmentioning

confidence: 99%

Search Space Analysis of Evolvable Robot Morphologies

Miras

Haasdijk

Glette

et al. 2018

Applications of Evolutionary Computation

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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.

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“…In a previous experiment, simple sinusoidal wave functions were implemented to control simulated servo modules (Veenstra et al, 2017). The same sinusoidal patterns are implemented here though they are implemented in a network of neurons.…”

Section: Neural Networkmentioning

confidence: 99%

“…The generative encoding was based on a context sensitive Lindenmayer-System (L-System) (Lindenmayer, 1968;Lindenmayer and Jürgensen, 1992;Prusinkiewicz and Lindenmayer, 1997)-a parallel rewriting system-as implemented in Veenstra et al (2017). The variables of the L-System represented the different modules of the robot.…”

Section: Generative Encodingmentioning

confidence: 99%