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USC Develops Robot With Tactile Sensors That Can Outperform Humans

June 21st, 2012 Scott Jung Neurology, Rehab

Gerald Loeb and his team of engineers at the University of Southern California’s Viterbi School of Engineering have developed a robot with a sense of feeling. More specifically, it’s able to identify materials by touch. The robot works by using a special type of tactile sensor called BioTac, which is able to identify a variety of materials based on their texture. When the BioTac-equipped robot runs its fingers over a specific material, the sensors vibrate at specific frequencies, and the material is identified based on an algorithm that  matches the material’s vibration pattern.

It works much in the same way that our own fingers work in distinguishing if a piece of clothing is made from cotton or leather. In addition, the robot can also tell where and in which direction forces are applied to the fingertip, and also the temperature of the material/object it is touching.

Here’s a little more about how BioTac works:

Like the human finger, the group’s BioTac® sensor has a soft, flexible skin over a liquid filling. The skin even has fingerprints on its surface, greatly enhancing its sensitivity to vibration. As the finger slides over a textured surface, the skin vibrates in characteristic ways. These vibrations are detected by a hydrophone inside the bone-like core of the finger. The human finger uses similar vibrations to identify textures, but the BioTac is even more sensitive.

According to the team, they’ve managed to train the robot to identify 117 common materials with an astounding 95% accuracy with only five “exploratory movements” (sweeping a finger across a small swatch of material). That’s a pretty amazing technology that could someday find itself in advanced prostheses of the future.

More info from USC: Robots Get a Feel for the World at USC Viterbi

More info from SynTouch (company commercializing the technology): Syntouch – The BioTac, Biomimetic Tactile Sensor

Journal astract in Frontiers in Neurorobotics: Bayesian exploration for intelligent identification of textures

Scott Jung

Scott Jung (@scottjung) is a Silicon Valley-based medical and health technology reporter and advocate, with a focus on wearables, telemedicine, and health technology in emerging countries. He has represented Medgadget at CES and Digital Health Summit, TEDMED, Stanford Medicine X, and SXSW. Always on the lookout for innovative medical technology worldwide, Scott has been invited to visit Colombia, Poland, and many other countries around the world to share how medtech is enriching the lives of its people. Scott holds a B.S. degree in Biomedical Engineering from the University of Southern California and an M.S. degree in Medical Product Development Management from San José State University. Scott is always looking for the next big thing in medical technology and digital health. Interested in helping him transform lives? Get in touch with him at http://scottju.ng

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