Information processing via physical soft body
Open accessAbstract
Soft machines have recently gained prominence due to their inherent softness and the resulting safety and resilience in applications. However, these machines also have disadvantages, as they respond with complex body dynamics when stimulated. These dynamics exhibit a variety of properties, including nonlinearity, memory, and potentially infinitely many degrees of freedom, which are often difficult to control. Here, we demonstrate that these seemingly undesirable properties can in fact be assets that can be exploited for real-time computation. Using body dynamics generated from a soft silicone arm, we show that they can be employed to emulate desired nonlinear dynamical systems. First, by using benchmark tasks, we demonstrate that the nonlinearity and memory within the body dynamics can increase the computational performance. Second, we characterize our system's computational capability by comparing its task performance with a standard machine learning technique and identify its range of validity and limitation. Our results suggest that soft bodies are not only impressive in their deformability and flexibility but can also be potentially used as computational resources on top and for free.
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Some evidence points to the potential of mechanical properties of the arm to serve a direct computational role.
“Not only can mechanical properties of the arm facilitate computation, some evidence points to the potential of these properties to serve a direct computational role (Nakajima et al., 2013; Nakajima et al., 2014; Nakajima et al., 2015).”
From Lessons for Robotics From the Control Architecture of the Octopus (2022) · cites this paper as Nakajima et al., 2015
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“Not only can mechanical properties of the arm facilitate computation, some evidence points to the potential of these properties to serve a direct computational role (Nakajima et al., 2013; Nakajima et al., 2014; Nakajima et al., 2015).”
Background
- Authors
- Nakajima, K., Hauser, H., Li, T., & Pfeifer, R.
- Affiliations
- Kyoto University (JP)
- Bern University of Applied Sciences (CH)
- Shanghai Jiao Tong University (CN)
- Corresponding author
- Nakajima, Kohei
- Venue
- Scientific Reports
- Published
- 2015-05-27
- Volume
- 5
- Issue
- 1
- Pages
- 10487-10487
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC4444959
- OpenAlex
- W2268464208
- Cited by
- 324
- References
- 43
- Citations by year
- 2026: 39 · 2025: 52 · 2024: 43 · 2023: 34 · 2022: 38 · 2021: 43 · 2020: 25 · 2019: 23 · 2018: 14 · 2017: 8 · 2016: 4 · 2015: 1
- FWCI
- 4.48
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Scientific Reports (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Institutional Repositories DataBase (IRDB) (submittedVersion, cc-by)
- Kyoto University Research Information Repository (Kyoto University) (submittedVersion, cc-by)
- Explore Bristol Research (submittedVersion, cc-by)
- Bristol Research (University of Bristol) (submittedVersion, cc-by)
- Funders
- European Commission (231608)
- Japan Society for the Promotion of Science
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Soft Robotics and Applications
- Physical Sciences › Engineering › Mechanical Engineering › Advanced Materials and Mechanics
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Reservoir Computing
- Keywords
- soft machines, soft body dynamics, nonlinear dynamical systems, real-time processing, soft robotics
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