Mass-Spring Damper Array as a Mechanical Medium for Computation

  • Yuki YamanakaEmail author
  • Takaharu Yaguchi
  • Kohei Nakajima
  • Helmut Hauser
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11141)


Recently, it has been reported that the dynamics of mechanical structures can be used as a computational resource—also referred to as morphological computation. In particular soft materials have been shown to have the potential to be used for time series forecasting. Although most soft materials can be modeled by mass-spring systems, a limited number of researches has been performed on the computational capabilities of such systems. In this paper, we propose an array of masses linked in a grid-like structure by spring-damper connections to investigate systematically the influence of structural (size) and dynamic (stiffness, damping) parameters on the computational capabilities for time series forecasting. In addition, such a structure gives us a good approximation of two-dimensional elastic media, e.g., a rubber sheet, and therefore a direct pathway to potentially implement results in a real system. In particular, we compared the mass-spring array to echo state networks, which are standard machine learning techniques for this kind of problems and are also closely related to the underlying theoretical models applied when exploiting mechanical structures for computation. Our results suggest a clear connection of morphological features to computational capabilities.


Soft Robotics Morphological computation Reservoir computing Mass-spring system Recurrent neural network 

Supplementary material

Supplementary material 1 (mp4 34356 KB)


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© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Kobe UniversityKobeJapan
  2. 2.JST PRESTOKawaguchiJapan
  3. 3.The University of TokyoBunkyo-kuJapan
  4. 4.University of BristolBristolUK

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