Biodiversity in evolved voxel-based soft robots

Medvet, Eric; Bartoli, Alberto; Pigozzi, Federico; Rochelli, Marco

doi:10.1145/3449639.3459315

Cited by 35 publications

(39 citation statements)

References 44 publications

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“…For evolved VSRs, we also examined the resulting behaviors, performing a systematic analysis based on the features proposed by Medvet et al . (2021), which should capture the different gaits achieved by VSRs. We provide a brief description of the analysis pipeline and of the aforementioned features together with the obtained results in Section 5.5.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…The features we employed to capture a VSR behavior in locomotion (Medvet et al ., 2021) are based on the movement of the center of mass of the VSR over time and on the way the VSR touches the ground during gait, i.e., its footprints. Here we provide just a brief description of the feature extraction procedure—we refer the reader to (Medvet et al ., 2021) for further details.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…Finally, we widen the analysis and systematically characterize the behavior of the evolved robots, taking into account some behavioral features introduced in Medvet et al . (2021).…”

Section: Introductionmentioning

confidence: 99%

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Merging pruning and neuroevolution: towards robust and efficient controllers for modular soft robots

Nadizar

Medvet²,

Ramstad³

et al. 2022

The Knowledge Engineering Review

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Artificial neural networks (ANNs) can be employed as controllers for robotic agents. Their structure is often complex, with many neurons and connections, especially when the robots have many sensors and actuators distributed across their bodies and/or when high expressive power is desirable. Pruning (removing neurons or connections) reduces the complexity of the ANN, thus increasing its energy efficiency, and has been reported to improve the generalization capability, in some cases. In addition, it is well-known that pruning in biological neural networks plays a fundamental role in the development of brains and their ability to learn. In this study, we consider the evolutionary optimization of neural controllers for the case study of Voxel-based soft robots, a kind of modular, bio-inspired soft robots, applying pruning during fitness evaluation. For a locomotion task, and for centralized as well as distributed controllers, we experimentally characterize the effect of different forms of pruning on after-pruning effectiveness, life-long effectiveness, adaptability to new terrains, and behavior. We find that incorporating some forms of pruning in neuroevolution leads to almost equally effective controllers as those evolved without pruning, with the benefit of higher robustness to pruning. We also observe occasional improvements in generalization ability.

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Section: Experiments and Resultsmentioning

confidence: 99%

Section: Experiments and Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Merging pruning and neuroevolution: towards robust and efficient controllers for modular soft robots

Nadizar

Medvet²,

Ramstad³

et al. 2022

The Knowledge Engineering Review

Self Cite

View full text Add to dashboard Cite

show abstract

“…Similar research have been done recently with evolvable morphologies [ [Cheney et al, 2014], [Lipson et al, 2016], [Nygaard et al, 2017], [Cheney et al, 2018], [Nygaard et al, 2018], , [Liao et al, 2019], , [Goff et al, 2021], [Gupta et al, 2021], [Medvet et al, 2021]]. Among all the studies, several approaches have been proposed to mitigate the body and brain mismatch effect on the population.…”

Section: Evolvable Morphologymentioning

confidence: 65%

The Effects of Learning in Morphologically Evolving Robot Systems

Luo¹,

Stuurman²,

Tomczak³

et al. 2021

Preprint

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Simultaneously evolving morphologies (bodies) and controllers (brains) of robots can cause a mismatch between the inherited body and brain in the offspring. To mitigate this problem, the addition of an infant learning period by the so-called Triangle of Life framework has been proposed relatively long ago. However, an empirical assessment is still lacking to-date. In this paper we investigate the effects of such a learning mechanism from different perspectives. Using extensive simulations we show that learning can greatly increase task performance and reduce the number of generations required to reach a certain fitness level compared to the purely evolutionary approach. Furthermore, although learning only directly affects the controllers, we demonstrate that the evolved morphologies will be also different. This provides a quantitative demonstration that changes in the brain can induce changes in the body. Finally, we examine the concept of morphological intelligence quantified by the ability of a given body to learn. We observe that the learning delta, the performance difference between the inherited and the learned brain, is growing throughout the evolutionary process. This shows that evolution is producing robots with an increasing plasticity, that is, consecutive generations are becoming better and better learners which in turn makes them better and better at the given task. All in all, our results demonstrate that the Triangle of Life is not only a concept of theoretical interest, but a system architecture with practical benefits.

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“…The co-design of robot bodies and brains has been an active area of research for decades [18,25,12]. Brain and body co-design stands for producing a control policy and a morphology for a robotic system.…”

Section: Related Workmentioning

confidence: 99%

A Unified Substrate for Body-Brain Co-evolution

Pontes-Filho,

Walker,

Najarro

et al. 2022

Preprint

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A successful development of a complex multicellular organism took millions of years of evolution. The genome of such a multicellular organism guides the development of its body from a single cell, including its control system. Our goal is to imitate this natural process using a single neural cellular automaton (NCA) as a genome for modular robotic agents. In the introduced approach, called Neural Cellular Robot Substrate (NCRS), a single NCA guides the growth of a robot and the cellular activity which controls the robot during deployment. We also introduce three benchmark environments, which test the ability of the approach to grow different robot morphologies. We evolve the NCRS with covariance matrix adaptation evolution strategy (CMA-ES), and covariance matrix adaptation MAP-Elites (CMA-ME) for quality diversity and observe that CMA-ME generates more diverse robot morphologies with higher fitness scores. While the NCRS is able to solve the easier tasks in the benchmark, the success rate reduces when the difficulty of the task increases. We discuss directions for future work that may facilitate the use of the NCRS approach for more complex domains.

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Biodiversity in evolved voxel-based soft robots

Cited by 35 publications

References 44 publications

Merging pruning and neuroevolution: towards robust and efficient controllers for modular soft robots

Merging pruning and neuroevolution: towards robust and efficient controllers for modular soft robots

The Effects of Learning in Morphologically Evolving Robot Systems

A Unified Substrate for Body-Brain Co-evolution

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