Lamarckian Evolution of Simulated Modular Robots

M. Jelisavcic, Kyrre Glette, Evert Haasdijk, A.E. Eiben

Research output: Contribution to JournalArticleAcademicpeer-review

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 the 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.
Original languageEnglish
Article number9
Pages (from-to)1-15
Number of pages15
JournalFrontiers in Robotics and AI
Volume6
Issue numberFebruary
DOIs
Publication statusPublished - 18 Feb 2019

Keywords

  • Evolutionary Robotics
  • Artificial life
  • Lamarckian evolution
  • Modular robots
  • Online learning
  • Embodied evolution

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