A research team at Cornell University led by PhD student Patricia Xu, have published their work in Science Robotics, where they report to have developed a stretchable optical lace that connects soft robots to their environment in the same way that the nervous system does for humans.

Senior author of the paper, Organics Robotics Lab director at the university, and mechanical and aerospace engineering associate professor, Rob Shepherd, described the drive of their work.  "We want to have a way to measure stresses and strains for highly deformable objects, and we want to do it using the hardware itself, not vision," he said. "A good way to think about it is from a biological perspective. A blind person can still feel because they have sensors in their fingers that deform when their finger deforms. Robots don't have that right now."

About a year ago, Shepherd was able to create sensory foams that detected deformations via optical fibers.  This time, Xu used a 3D printed polyurethane lattice structure.  In its core are stretchable optical fibers with more than 12 mechanosensors and an LED light for visual of the fibers.

When pressure is applied onto the structure, sensors would indicate where changes flow.  "When the structure deforms, you have contact between the input line and the output lines, and the light jumps into these output loops in the structure, so you can tell where the contact is happening," said Xu. "The intensity of this determines the intensity of the deformation itself."

Instead of using it as a sleeve for the robot, it would act as the robot flesh itself.  The researchers see it useful in healthcare applications and in manufacturing.

Compared to the skin on our fingertips, the optical lace is definitely less sensitive.  However, compared to our backs, it is more sensitive as it is filled with nerve receptors.  Aside from being stretchable, an added advantage is that it is washable.  Because of this, the researchers are planning to commercialize the sensors to be incorporated in garments to be used in augmented reality training.