ArticleFrontiers in robotics and AI2019
Lamarckian Evolution of Simulated Modular Robots.
Article in Frontiers in robotics and AI, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Enhancing robot evolution through Lamarckian principles.Scientific reports · 2023Article
- Artificial evolution of robot bodies and control: on the interaction between evolution, learning and culture.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2022Article
- The Effects of Learning in Morphologically Evolving Robot Systems.Frontiers in robotics and AI · 2022Article
- Embodied intelligence via learning and evolution.Nature communications · 2021Article
- Real-World Robot Evolution: Why Would it (not) Work?Frontiers in robotics and AI · 2021Review
- MAP-Elites Enables Powerful Stepping Stones and Diversity for Modular Robotics.Frontiers in robotics and AI · 2021Article
- Foundations of Erobotics.International journal of social robotics · 2021Article
- New Biological Morphogenetic Methods for Evolutionary Design of Robot Bodies.Frontiers in bioengineering and biotechnology · 2020Review
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Authors and funding
4 authors.
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Abstract
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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