Scientists at the University of Southern California's Viterbi School of Engineering specially designed robot who can outperform humans in identifying a wide range of natural materials according to their textures, paving the way for advancements in prostheses, personal assistance robots and consumer product testing.
With the right sensors, actuators and software, robots can be given the sense of feel, or at least the ability to identify different materials by touch.
A famous theorem by 18th century mathematician Thomas Bayes describes how decisions might be made from the information obtained during these movements. Until now, however, there was no way to decide which exploratory movement to make next. The article, authored by Professor of Biomedical Engineering, Gerald Loeb and recently graduated doctoral student Jeremy Fishel, describes their new theorem for solving this general problem as "Bayesian Exploration." Built by Fishel, the specialized robot has been trained on 117 common materials gathered from fabric, stationery and hardware stores.
The robot is very good at identifying objects with the textures (almost 95% efficiency), it still does not have a way to telling what textures people will prefer.
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 robot finger is even more sensitive. When humans try to identify an object by touch, they use a wide range of exploratory movements based on their prior experience with similar objects.
The study was published on June 18 in 'Frontiers in Neurorobotics'.