Evolutionary Robotics: What, Why, and Where to

Doncieux, Stéphane; Bredèche, Nicolas; Mouret, Jean-Baptiste; Eiben, A. E.

doi:10.3389/frobt.2015.00004

Cited by 217 publications

(177 citation statements)

References 132 publications

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“…Evolutionary robotics (Nolfi and Floreano, 2000;Sims, 1994;Floreano et al, 2008;Doncieux et al, 2015) applies concepts derived from biology to the design of robotic systems. It has demonstrated an ability to address challenging problems in many task domains including: gaits (Clune et al, 2009), object manipulation (Bongard, 2008), biological study (Crespi et al, 2013;Doorly et al, 2009), and the optimization of morphology (Auerbach and Bongard, 2010;Bongard, 2010;Cheney et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Lexicase selection outperforms previous strategies for incremental evolution of virtual creature controllers

Moore¹,

Stanton²

2017

Proceedings of the 14th European Conference on Artificial Life ECAL 2017

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Evolving robust behaviors for robots has proven to be a challenging problem. Determining how to optimize behavior for a specific instance, while also realizing behaviors that generalize to variations on the problem often requires highly customized algorithms and problem-specific tuning of the evolutionary platform. Algorithms that can realize robust, generalized behavior without this customization are therefore highly desirable. In this paper, we examine the Lexicase selection algorithm as a possible general algorithm for a wall crossing robot task. Previous work has resulted in specialized strategies to evolve robust behaviors for this task. Here, we show that Lexicase selection is not only competitive with these strategies but after parameter tuning, actually exceeds the performance of the specialized algorithms.

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Section: Introductionmentioning

confidence: 99%

Lexicase selection outperforms previous strategies for incremental evolution of virtual creature controllers

Moore¹,

Stanton²

2017

Proceedings of the 14th European Conference on Artificial Life ECAL 2017

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“…Similarly, previous work has demonstrated the benefits of maintaining genotypic diversity as a means to boost the quality of evolved behaviors for various ER tasks (Floreano et al, 2008;Doncieux et al, 2011Doncieux et al, , 2015Mouret and Doncieux, 2012). Genotype diversity maintenance is also a well-explored topic in more general evolutionary computation research (Brameier and Banzhaf, 2002;Ekárt and Németh, 2002;Crepinsek et al, 2013;Lehman et al, 2013;Mueller-Bady et al, 2016).…”

Section: Behavioral and Genotypic Diversity Maintenancementioning

confidence: 99%

“…Recent work in Evolutionary Robotics (ER) (Doncieux et al, 2015) has provided increasing empirical evidence that maintaining diversity in genotypes (robot controller encodings) and phenotypes (robot behaviors) improves the quality (task performance) of evolved behaviors in a range of tasks (Mouret and Doncieux, 2012;Cully et al, 2015;Cully and Mouret, 2016;Gomes et al, 2016).…”

Section: Behavioral and Genotypic Diversity Maintenancementioning

confidence: 99%

“…Recent work in Evolutionary Robotics (ER) (Doncieux et al, 2015) has provided increasing empirical evidence that maintaining diversity in phenotypes (robot behaviors) improves the quality (task performance) of evolved behaviors (Mouret and Doncieux, 2012;Cully et al, 2015;Cully and Mouret, 2016;Gomes et al, 2016). Specifically, replacing objective search with the search for behavioral diversity in controller evolution (Moriguchi and Honiden, 2010;Mouret and Doncieux, 2012;Lehman et al, 2013;Gomes et al, 2015) has been demonstrated to boost the quality of evolved behaviors across a range of simulated (Lehman and Stanley, 2011a;Mouret and Doncieux, 2012;Gomes et al, 2016) and physical (Cully et al, 2015;Cully and Mouret, 2016) ER tasks.…”

Section: Introductionmentioning

confidence: 99%

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Evolutionary Policy Transfer and Search Methods for Boosting Behavior Quality: RoboCup Keep-Away Case Study

Nitschke

Didi

2017

Front. Robot. AI

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This study evaluates various evolutionary search methods to direct neural controller evolution in company with policy (behavior) transfer across increasingly complex collective robotic (RoboCup keep-away) tasks. Robot behaviors are first evolved in a source task and then transferred for further evolution to more complex target tasks. Evolutionary search methods tested include objective-based search (fitness function), behavioral and genotypic diversity maintenance, and hybrids of such diversity maintenance and objective-based search. Evolved behavior quality is evaluated according to effectiveness and efficiency. Effectiveness is the average task performance of transferred and evolved behaviors, where task performance is the average time the ball is controlled by a keeper team. Efficiency is the average number of generations taken for the fittest evolved behaviors to reach a minimum task performance threshold given policy transfer. Results indicate that policy transfer coupled with hybridized evolution (behavioral diversity maintenance and objective-based search) addresses the bootstrapping problem for increasingly complex keep-away tasks. That is, this hybrid method (coupled with policy transfer) evolves behaviors that could not otherwise be evolved. Also, this hybrid evolutionary search was demonstrated as consistently evolving topologically simple neural controllers that elicited high-quality behaviors.

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“…Evolutionary robotics relies on stochastic algorithms to generate robot controllers or morphology [13]. They have been used to generate controllers for locomotion, navigation or foraging tasks [14].…”

Section: Related Workmentioning

confidence: 99%

Bootstrapping interactions with objects from raw sensorimotor data: A novelty search based approach

Maestre

Cully

Gonzales

et al. 2015

2015 Joint IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob)

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Abstract-Determining in advance all objects that a robot will interact with in an open environment is very challenging, if not impossible. It makes difficult the development of models that will allow to perceive and recognize objects, to interact with them and to predict how these objects will react to interactions with other objects or with the robot. Developmental robotics proposes to make robots learn by themselves such models through a dedicated exploration step. It raises a chicken-and-egg problem: the robot needs to learn about objects to discover how to interact with them and, to this end, it needs to interact with them. In this work, we propose Novelty-driven Evolutionary Babbling (NovEB), an approach enabling to bootstrap this process and to acquire knowledge about objects in the surrounding environment without requiring to include a priori knowledge about the environment, including objects, or about the means to interact with them. Our approach consists in using an evolutionary algorithm driven by a novelty criterion defined in the raw sensorimotor flow: behaviours, described by a trajectory of the robot end effector, are generated with the goal to maximize the novelty of raw perceptions. The approach is tested on a simulated PR2 robot and is compared to a random motor babbling.

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Evolutionary Robotics: What, Why, and Where to

Cited by 217 publications

References 132 publications

Lexicase selection outperforms previous strategies for incremental evolution of virtual creature controllers

Lexicase selection outperforms previous strategies for incremental evolution of virtual creature controllers

Evolutionary Policy Transfer and Search Methods for Boosting Behavior Quality: RoboCup Keep-Away Case Study

Bootstrapping interactions with objects from raw sensorimotor data: A novelty search based approach

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