Exploiting the dynamics of soft materials for machine learning

Kohei Nakajima*, Helmut Hauser, Tao Li, Rolf Pfeifer

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

76 Citations (Scopus)
586 Downloads (Pure)


Soft materials are increasingly utilized for various purposes in many engineering applications. These materials have been shown to perform a number of functions that were previously difficult to implement using rigid materials. Here, we argue that the diverse dynamics generated by actuating soft materials can be effectively used for machine learning purposes. This is demonstrated using a soft silicone arm through a technique of multiplexing, which enables the rich transient dynamics of the soft materials to be fully exploited as a computational resource. The computational performance of the soft silicone arm is examined through two standard benchmark tasks. Results show that the soft arm compares well to or even outperforms conventional machine learning techniques under multiple conditions. We then demonstrate that this system can be used for the sensory time series prediction problem for the soft arm itself, which suggests its immediate applicability to a real-world machine learning problem. Our approach, on the one hand, represents a radical departure from traditional computational methods, whereas on the other hand, it fits nicely into a more general perspective of computation by way of exploiting the properties of physical materials in the real world.

Original languageEnglish
Pages (from-to)339-347
Number of pages9
JournalSoft Robotics
Issue number3
Early online date30 Apr 2018
Publication statusPublished - 1 Jun 2018


  • physical reservoir computing
  • soft robotics
  • physical computation
  • octopus


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