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A.E. Eiben
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Proceedings Papers
. isal, ALIFE 2021: The 2021 Conference on Artificial Life26, (July 19–23, 2021) doi: 10.1162/isal_a_00404
Abstract
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Moving around in the environment is a fundamental skill for mobile robots. This makes the evolution of an appropriate gait, a pivotal problem in evolutionary robotics. Whereas the majority of the related studies concern robots with predefined modular or legged morphologies and locomotion speed as the optimization objective, here we investigate robots with evolvable morphologies and behavioral traits included in the fitness function. To analyze the effects we consider morphological as well as behavioral features of the evolved robots. To this end, we introduce novel behavioral measures that describe how the robot locomotes and look into the trade-off between them. Our main goal is to gain insights into differences in possible gaits of modular robots and to provide tools to steer evolution towards objectives beyond ‘simple’ speed.
Proceedings Papers
. isal, ALIFE 2021: The 2021 Conference on Artificial Life25, (July 19–23, 2021) doi: 10.1162/isal_a_00371
Abstract
PDF
In this work, we evolve phenotypically plastic robots - robots that adapt their bodies and brains according to environmental conditions - in changing environments. In particular, we investigate how the possibility of death in early environmental conditions impacts evolvability and robot traits. Our results demonstrate that early-death improves the efficiency of the evolutionary process for the earlier environmental conditions. On the other hand, the possibility of early-death in the earlier environmental conditions results in a dramatic loss of performance in the latter environmental conditions.
Proceedings Papers
. isal2019, ALIFE 2019: The 2019 Conference on Artificial Life396-403, (July 29–August 2, 2019) doi: 10.1162/isal_a_00192
Abstract
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This paper studies the effects of changing environments on the evolution of bodies and brains of modular robots. Our results indicate that environmental history has a long lasting impact on the evolved robot properties. We show that if the environment gradually changes from type A to type B, then the evolved morphological and behavioral properties are very different from those evolving in a type B environment directly. That is, we observe some sort of “genetic memory”. Furthermore, we show that gradually introducing a difficult environment helps to reach fitness levels that are higher than those obtained under those difficult conditions directly. Finally, we also demonstrate that robots evolved in gradually changing environments are more robust, i.e., exhibit a more stable performance under different conditions.
Proceedings Papers
. alife2018, ALIFE 2018: The 2018 Conference on Artificial Life224-231, (July 23–27, 2018) doi: 10.1162/isal_a_00047
Abstract
PDF
This paper investigates the evolution of modular robots using different selection preferences (i.e., fitness functions), aiming at novelty, speed of locomotion, number of limbs, and combinations of these. The outcomes are analyzed from different perspectives: sampling of the search space, evolved morphologies, and evolved behaviors. This results in a wealth of findings, including a surprise about the number of sampled regions of the search space and the effect of different fitness functions on the evolved morphologies.
Proceedings Papers
. alife2018, ALIFE 2018: The 2018 Conference on Artificial Life327-334, (July 23–27, 2018) doi: 10.1162/isal_a_00063
Abstract
PDF
The evolution of robots, when applied to both the morphologies and the controllers, is not only a means to obtain highquality robot designs, but also a process that results in many body brain-fitness data points. Inspired by this perspective, in this paper we investigate the relative importance of robot bodies and brains for a good fitness. We introduce a method to isolate and quantify the effect of the bodies and brains on the quality of the robots and perform a case study. The method is general in that it is not restricted to evolutionary systems. for the case study, we use a system of modular robots, where the bodies are evolvable and the brains are evolvable and learnable. These case studies validate the usefulness of our method and deliver interesting insights into the interplay between bodies and brains in evolutionary robotics.
Proceedings Papers
Jacqueline Heinerman, Jörg Stork, Margarita Alejandra Rebolledo Coy, Julien Hubert, Thomas Bartz-Beielstein ...
. ecal2017, ECAL 2017, the Fourteenth European Conference on Artificial Life200-207, (September 4–8, 2017) doi: 10.1162/isal_a_036
Abstract
PDF
Social learning enables multiple robots to share learned experiences while completing a task. The literature offers contradicting examples of its benefits; robots trained with social learning reach a higher performance, an increased learning speed, or both, compared to their individual learning counterparts. No general explanation has been advanced for the difference in observations, which make the results highly dependent on the particular system and parameter setting. In this research, we show that even within one system, the observed advantages of social learning can vary between parameter settings. Using Evolutionary Robotics, we train robots individually in a foraging task. We compare the performance of 50 parameter instances of the evolutionary algorithm obtained by a definitive screening design. We apply social learning in groups of two and four robots to the parameter settings that lead to the best and median performance. Our results show that the observed advantages of social learning differ highly between parameter settings but in general, median quality parameter settings experience more benefit from social learning. These results serve as a reminder that tuning of the parameters should not be left as an afterthought because they can drastically impact the conclusions on the advantages of social learning. Additionally, these results suggest that social learning reduces the sensitivity of the learning process to the choice of parameters.