Image of Soft Robot.
The system includes a motion capture system, soft sensors, a neural network, and a soft robotic finger. (Credit: UCSD)

A perception system for soft robots was inspired by the way humans process information about their own bodies in space and in relation to other objects and people. The system includes a motion capture system, soft sensors, a neural network, and a soft robotic finger.

The researchers’ ultimate goal is to build a system that can predict a robot’s movements and internal state without relying on external sensors, much like humans do every day. The work has applications in human-robot interaction and wearable robotics, as well as soft devices to correct disorders affecting muscles and bones.

The system is meant to mimic the various components required for humans to navigate their environment: the motion capture system stands in for vision; the neural network stands in for brain functions; the sensors for touch; and the finger for the body interacting with the outside world. The motion capture system is there to train the neural network and can be discarded once training is complete.

Researchers embedded soft strain sensors arbitrarily within the soft robotic finger, knowing that they would be responsive to a wide variety of motions, and used machine learning techniques to interpret the sensors’ signals. This allowed the team to predict forces applied to, and movements of, the finger. This approach will enable researchers to develop models that can predict forces and deformations experienced by soft robotic systems as they move.

This is important because the techniques traditionally used in robotics for processing sensor data can’t capture the complex deformations of soft systems. In addition, the information the sensors capture is equally complex. As a result, sensor design, placement, and fabrication in soft robots are difficult tasks that could be vastly improved if researchers had access to robust models. This is what the research team is hoping to provide.

Next steps include scaling up the number of sensors to better mimic the dense sensing capabilities of biological skin and closing the loop for feedback control of the actuator.

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