Soft-body dynamics induces energy efficiency in undulatory swimming: A deep learning study

Guanda Li, Jun Shintake, Mitsuhiro Hayashibe

Research output: Contribution to journalArticlepeer-review


Recently, soft robotics has gained considerable attention as it promises numerous applications thanks to unique features originating from the physical compliance of the robots. Biomimetic underwater robots are a promising application in soft robotics and are expected to achieve efficient swimming comparable to the real aquatic life in nature. However, the energy efficiency of soft robots of this type has not gained much attention and has been fully investigated previously. This paper presents a comparative study to verify the effect of soft-body dynamics on energy efficiency in underwater locomotion by comparing the swimming of soft and rigid snake robots. These robots have the same motor capacity, mass, and body dimensions while maintaining the same actuation degrees of freedom. Different gait patterns are explored using a controller based on grid search and the deep reinforcement learning controller to cover the large solution space for the actuation space. The quantitative analysis of the energy consumption of these gaits indicates that the soft snake robot consumed less energy to reach the same velocity as the rigid snake robot. When the robots swim at the same average velocity of 0.024 m/s, the required power for the soft-body robot is reduced by 80.4% compared to the rigid counterpart. The present study is expected to contribute to promoting a new research direction to emphasize the energy efficiency advantage of soft-body dynamics in robot design.

Original languageEnglish
Article number1102854
JournalFrontiers in Robotics and AI
Publication statusPublished - 2023 Feb 9


  • deep reinforcement learning
  • energy efficiency
  • snake robot
  • soft robot
  • underwater robot

ASJC Scopus subject areas

  • Computer Science Applications
  • Artificial Intelligence


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