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Machine Learning Application to Soft Robots Systems

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Robots built from rigid components and sensors have limitations with the flexibility that they can exhibit for you to control them. Innovative solutions opened doors for creating soft systems to cope with problems brought by these robots. This soft feature leads us to what you know now as soft robotics.

These robots have significantly escalated as the scientific community seeks for an evolution toward human-friendly robotics. With Bio-inspiration and Automation as its two forerunners, robotic works actuated soft robots as systems designed from materials built from mechanical properties artificially assembled to mimic our living tissues.

Soft robots come from soft elements like shape memory alloys, soft smart frameworks, and even fluids, which can help form soft actuators. Embedded sensors placed on the new soft materials allows a robot to function the same way, but with great flexibility.

On the other hand, automation works to incorporate the concepts of machine learning and robotics. Aside from controlling these robots, we also need to have them learn about the real world we live in and eventually about themselves. “How is this possible?” you ask.

Machine learning helps robots to:

  • learn and adapt to situations that are not familiar to them
  • navigate itself to perform tasks

Compared to previous rigid robots, soft robots are less predictable when it comes to their reactions to the environment, so the automation works to improve its design and core functions.

Reservoir Computing for Pneumatic Artificial Muscles Pressurized Fluid Systems

The most convenient way to create soft robots is through a pressurized fluid. It allows movement by contraction and expansion, which imitates how your muscles react to stimulation.

One of these systems that use the fluidic medium is a robot with Pneumatic Artificial Muscles (PAM). According to a graduate school associate professor Kohei Nakajima, PAMs are:

  • fluid-driven systems which expand and contract to move
  • made from rubber and fibre
  • inherently suffers from material stress like Random Mechanical Noise and Hysteresis.
  • innovations of previous rigid sensors from laser-based monitors

Given that PAMs have a dynamic nature, Nakajima and his team adhered to an excellent machine learning technique called Reservoir Computing. This method involves feeding information about a system into a unique neural network in real-time, leading you to an ever-changing model as it adapts to the environment.

Moreover, machine learning techniques like reservoir computing makes it possible for a new generation of human-friendly technology, such as:

  • biomedical robots
  • wearable rehabilitation devices

Other than this, Nakajima also added that their study for reservoir computing could be useful in more applications, like in remote sensing. More researchers added that studies in neuromorphic computing could also incorporate these ideas to improve its system’s performance and efficiency. Soft robotics allows such technology to operate safely in our environment.

Like any innovation created, soft robotics also comes with many challenges limiting its adoption in today’s industries. On the bright side, these robots suggest many possibilities for improvements in the years to come. Soft robotics can be a means for humans and technology to come together.

How do soft robotics help you develop your thesis?

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