Robohub.org
 

A soft matter computer for soft robots


by
03 October 2019



share this:

Our work published recently in Science Robotics describes a new form of computer, ideally suited to controlling soft robots. Our Soft Matter Computer (SMC) is inspired by the way information is encoded and transmitted in the vascular system.

Soft robotics has exploded in popularity over the last decade. In part, this is because robots made with soft materials can easily adapt and conform to their environment. This makes soft robots particularly suited to tasks that require a delicate touch, such as handling fragile materials or operating close to the (human) body.

However, until now, most soft robotic systems have been controlled by conventional electronics, made from hard materials such as silicon. This means putting stiff components into an otherwise soft system, limiting its overall flexibility. Our SMC instead uses only flexible materials, allowing soft robots to retain the many benefits of softness. Here’s how it works.

The building block of our soft matter computer is the conductive fluid receptor (CFR). A CFR consists of two electrodes, placed on opposite sides of a soft tube, parallel to the direction of fluid flow. We inject a pattern of insulating (air, clear) and conducting (saltwater, red) fluids into the CFR. When the saltwater connects the two electrodes, the CFR is switched on. By connecting a soft actuator to a CFR, we have a simple control system.

By connecting multiple CFRs together, we can create SMCs that perform more complex calculations. In our paper, we show SMC architectures for performing both analogue and digital computation. This means that in theory, SMCs could be used to implement any algorithm used on an electronic computer.

SMCs can be easily integrated into the body of a soft robot. For example, softworms [1] are powered by two shape memory alloy (SMA) actuators. These actuators contract when current flows through them; by controlling the activation pattern of the two actuators, three distinct gaits can be produced. We show that we can integrate an SMC into the body of a softworm and produce each of the three gaits by varying the programming of the SMC. The video below shows an SMC-Softworm, with the saltwater dyed red.

The SMC is not the first soft matter control system designed for soft robots. Other research groups have developed fluidic [2] and microfluidic [3, 4] control systems. These approaches, however, are limited to controlling fluidic actuators. The SMC outputs an electrical current, meaning it can interface with most soft actuators.

A grand challenge for soft robotics is the development of an autonomous and intelligent robotic system fabricated entirely out of soft materials. We believe that the SMC is an important step towards such a system, while also enabling new possibilities in environmental monitoring, smart prosthetic devices, wearable biosensing and self-healing composites.

You can read more about this work in the Science Robotics paper “A soft matter computer for soft robots”, by M. Garrad, G. Soter, A.T. Conn, H. Hauser, and J. Rossiter.

[1] Umedachi, T., V. Vikas, and B. A. Trimmer. “Softworms: the design and control of non-pneumatic, 3D-printed, deformable robots.” Bioinspiration & biomimetics 11.2 (2016): 025001.
[2] Preston, Daniel J., et al. “Digital logic for soft devices.” Proceedings of the National Academy of Sciences 116.16 (2019): 7750-7759.
[3] Wehner, Michael, et al. “An integrated design and fabrication strategy for entirely soft, autonomous robots.” Nature536.7617 (2016): 451.
[4] Mahon, Stephen T., et al. “Soft Robots for Extreme Environments: Removing Electronic Control.” 2019 2nd IEEE International Conference on Soft Robotics (RoboSoft). IEEE, 2019.



tags:


Martin Garrad is a PhD student with the EPSRC Centre for Doctoral Training in Future Autonomous and Robotic Systems (FARSCOPE), based at the Bristol Robotics Laboratory.
Martin Garrad is a PhD student with the EPSRC Centre for Doctoral Training in Future Autonomous and Robotic Systems (FARSCOPE), based at the Bristol Robotics Laboratory.

            AUAI is supported by:



Subscribe to Robohub newsletter on substack



Related posts :

New research enables a robot to chart a better course

  17 Jun 2026
By rapidly generating a smooth path plan that cuts travel time and avoids obstacles, the open-source “MIGHTY” system could streamline disaster recovery and parcel delivery.

Entangled robotic matter with cohesive motion

  15 Jun 2026
Engineers have developed a robotic collective that behaves less like a machine and more like a material that flows.

Robot Talk Episode 160 – Robotic blacksmiths, with Edward Mehr

  12 Jun 2026
In the latest episode of the Robot Talk podcast, Claire chatted to Edward Mehr from Machina Labs about their RoboCraftsman that shapes complex metal parts for the aerospace, defence, and automotive industries.

Congratulations to the #AAMAS2026 best paper award winners

  08 Jun 2026
Find out who won in the categories of best paper, best student paper, and best blue sky paper.

Robot Talk Episode 159 – Robot sensing and manipulation, with Maria Koskinopoulou

  05 Jun 2026
In the latest episode of the Robot Talk podcast, Claire chatted to Maria Koskinopoulou from Heriot-Watt University about autonomous robotic manipulators for surgery, industry, and beyond.

Global robotics technology roadmap

  03 Jun 2026
A multi-regional, cross-domain strategic perspective for Europe, Asia, and the United States.

RoboChem Flex: democratisation of the autonomous synthesis robot

  02 Jun 2026
A versatile, modular design and the option for "human-in-the-loop" analytics.

Robot Talk Episode 158 – Autonomous robot deliveries, with Ahti Heinla

  29 May 2026
In the latest episode of the Robot Talk podcast, Claire chatted to Ahti Heinla from Starship Technologies about their AI-powered delivery robots that operate independently on streets and pavements.



AUAI is supported by:







Subscribe to Robohub newsletter on substack




 















©2026.05 - Association for the Understanding of Artificial Intelligence