Learning in Growing Robots: Knowledge Transfer from Tadpole to Frog Robot

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Inspired by natural growing processes, we investigate how morphological changes can potentially help to lead and facilitate the task of learning to control a robot. We use the model of a tadpole that grows in four discrete stages into a frog. The control task to learn is to locomote to food positions that occur at random positions. We employ reinforcement learning, which is able to find a tail-driven swimming strategy for the tadpole stage that transitions into a leg-driven strategy for the frog. Furthermore, by using knowledge transferred from one growing stage to the next one, we were able to show that growing can benefit from guiding the controller optimization through morphological changes. The results suggest that learning time can be reduced compared to the cases when learning each stage individually from scratch.
Original languageEnglish
Title of host publicationConference on Biomimetic and Biohybrid Systems
Subtitle of host publicationLiving Machines 2019: Biomimetic and Biohybrid Systems
PublisherSpringer, Cham
Number of pages5
ISBN (Print)9783030247409
Publication statusPublished - 6 Jul 2019
Event2019 Living Machines - Kasugano International Forum, Nara, Japan
Duration: 9 Jul 201912 Jul 2019
Conference number: 8

Publication series

NameLecture Notes in Computer Science
PublisherSpringer Cham


Conference2019 Living Machines
Internet address

Structured keywords

  • Tactile Action Perception


  • morphological computation
  • reinforcement learning
  • knowledge transfer
  • biomimetic robotics

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