USC Develops Robot With Tactile Sensors That Can Outperform Humans

USC Develops Robot With Tactile Sensors That Can Outperform Humans

What does a robot feel when it touches something? Little or nothing until now. But with the right sensors, actuators and software, robots can be given the sense of feel – or at least the ability to identify materials by touch.

Researchers at the University of Southern California’s Viterbi School of Engineering published a study today in Frontiers in

A robot hand equipped with SynTouch’s BioTac sensors.

Neurorobotics showing that a specially designed robot can outperform humans in identifying a wide range of natural materials according to their textures, paving the way for advancements in prostheses, personal assistive robots and consumer product testing.

The robot was equipped with a new type of tactile sensor built to mimic the human fingertip. It also used a newly designed algorithm to make decisions about how to explore the outside world by imitating human strategies. Capable of other human sensations, the sensor can also tell where and in which direction forces are applied to the fingertip and even the thermal properties of an object being touched.

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.

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. 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 this general problem as “Bayesian Exploration.”

Built by Fishel, the specialized robot was trained on 117 common materials gathered from fabric, stationery and hardware stores. When confronted with one material at random, the robot could correctly identify the material 95% of the time, after intelligently selecting and making an average of five exploratory movements. It was only rarely confused by a pair of similar textures that human subjects making their own exploratory movements could not distinguish at all.

So, is touch another task that humans will outsource to robots? Fishel and Loeb point out that while their robot is very good at identifying which textures are similar to each other, it has no way to tell what textures people will prefer. Instead, they say this robot touch technology could be used in human prostheses or to assist companies who employ experts to judge the feel of consumer products and even human skin.

Robots Get A Feel For The World from USC Viterbi on Vimeo.

Loeb and Fishel are partners in SynTouch LLC, which develops and manufactures tactile sensors for mechatronic systems that mimic the human hand. Founded in 2008 by researchers from USC’s Medical Device Development Facility, the start-up is now selling their BioTac sensors to other researchers and manufacturers of industrial robots and prosthetic hands.

Another paper from this research group in the same issue of Frontiers in Neurorobotics describes the use of their BioTac sensor to identify the hardness of materials like rubber.

Original funding for development of the sensor was provided by the Keck Futures Initiative of the National Academy of Sciences to develop a better prosthetic hand for amputees. SynTouch also received a grant from the National Institutes of Health to integrate BioTac sensors with such prostheses. The texture discrimination project was funded by the U.S. Defense Advanced Research Projects Agency (DARPA) and the material hardness study by the National Science Foundation.

Fishel just completed his doctoral dissertation in biomedical engineering based on the texture research. Loeb, also Director of the USC Medical Device Development Facility, holds 54 U.S. Patents and has published over 200 journal articles on topics ranging from cochlear implants for the deaf to fundamental studies of muscles and nerves.

A joint press release between SynTouch and USC has received widespread coverage, being featured in Time, Gizmodo, Engadget, Gizmag, PopSci, PCWorld, MSNBC, Wired and many other websites.

The technology developed by SynTouch enables a robot equipped with BioTacs to discriminate between 117 different textures with greater than 95% accuracy. The system was even shown to outperform human subjects in discriminating between difficult pairs of textures.

Research Article:

Fishel, J.A., Loeb, G.E., Bayesian exploration for intelligent identification of textures. Frontiers in Neurorobotics 6:4, 2012.

In order to endow robots with human-like abilities to characterize and identify objects, they must be provided with tactile sensors and intelligent algorithms to select, control, and interpret data from useful exploratory movements. Humans make informed decisions on the sequence of exploratory movements that would yield the most information for the task, depending on what the object may be and prior knowledge of what to expect from possible exploratory movements. This study is focused on texture discrimination, a subset of a much larger group of exploratory movements and percepts that humans use to discriminate, characterize, and identify objects. Using a testbed equipped with a biologically inspired tactile sensor (the BioTac), we produced sliding movements similar to those that humans make when exploring textures. Measurement of tactile vibrations and reaction forces when exploring textures were used to extract measures of textural properties inspired from psychophysical literature (traction, roughness, and fineness). Different combinations of normal force and velocity were identified to be useful for each of these three properties. A total of 117 textures were explored with these three movements to create a database of prior experience to use for identifying these same textures in future encounters. When exploring a texture, the discrimination algorithm adaptively selects the optimal movement to make and property to measure based on previous experience to differentiate the texture from a set of plausible candidates, a process we call Bayesian exploration. Performance of 99.6% in correctly discriminating pairs of similar textures was found to exceed human capabilities. Absolute classification from the entire set of 117 textures generally required a small number of well-chosen exploratory movements (median = 5) and yielded a 95.4% success rate. The method of Bayesian exploration developed and tested in this paper may generalize well to other cognitive problems.

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.

source : http://viterbi.usc.edu/news/news/2012/robots-get-a.htm

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