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Video: Robots Develop Sense of Touch

Ann R. Thryft

July 6, 2012

3 Min Read
Video: Robots Develop Sense of Touch

The ability of robots to grasp different objects, recognize human gestures, and even guide surgical tools is increasing at a fast pace. A new combination of sensors, actuators, and software will give them a sense of touch for identifying textures that's even more finely tuned than a human's.

In what could be a major advance for both industrial robots and prosthetic hands, researchers at the USC Viterbi School of Engineering have developed a specially designed robot that can outperform humans in identifying a wide range of natural materials by sensing their textures. The robot has a new type of tactile sensor built to mimic the human fingertip. It uses algorithms imitating human strategies to identify textures by touch.


Built by Gerald Loeb, professor of biomedical engineering and director of the USC Medical Device Development Facility, and biomedical engineering doctoral candidate Jeremy Fishel, the multimodal BioTac fingertip-shaped sensor is filled with conductive fluid. Fingerprint-like ridges on its flexible elastomeric "skin" have a biomimetic size of 0.4mm spacing. When the BioTac slides over textured surfaces, vibrations are induced that propagate through the fluid, changing its impedance, which is sensed by electrodes. That data is conveyed to a pressure sensor.

The testbed apparatus consists of a stepper motor attached to a lever that raises or lowers the BioTac on or off of textured surfaces. Contact force is controlled by adjusting the stepper motor's vertical position. A special vibration-free linear stage is used to slide textures past the BioTac to emulate lateral motion. The textured surfaces are attached to flat, square magnets that can be quickly mounted and dismounted on a steel plate attached to the linear stage (watch a video below).

The BioTac sensor selects from a database of 117 different textures derived from common materials. The database serves as the equivalent of previous experiences the robot can use for distinguishing new textures it encounters. After selecting and making an average of five exploratory movements, the robot could correctly identify novel materials 95 percent of the time. Compared to human subjects who could not distinguish between two very similar textures, the robot was successful 99.6 percent of the time.

To differentiate the new texture from a set of plausible candidates, the discrimination algorithm uses a method when exploring a texture to adaptively select the best movement to make and the correct property to measure, based on its previous experience. Loeb and Fishel call this process "Bayesian exploration" in an article describing BioTac published in Frontiers in Neurorobotics.

"Extending this algorithm to a complete robotic system working in unstructured environments is expected to degrade the quality of measured signals, which was enhanced by the careful design of a custom-built experimental apparatus," they wrote.

"In particular, the actuators in humanoid robots are likely to be considerably noisier than our apparatus, introducing both variability into the exploratory movements and noise into the sensor signals. Additional training to better understand the characteristics of noise and variability is one way to compensate for this. We expect the Bayesian exploration method to be robust to this and evolve to make the most of available information."

The authors are also equity partners in SynTouch LLC, which develops and manufactures tactile sensors for mechatronic systems that mimic the human hand, including the BioTac.

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About the Author(s)

Ann R. Thryft

Ann R. Thryft has written about manufacturing- and electronics-related technologies for Design News, EE Times, Test & Measurement World, EDN, RTC Magazine, COTS Journal, Nikkei Electronics Asia, Computer Design, and Electronic Buyers' News (EBN). She's introduced readers to several emerging trends: industrial cybersecurity for operational technology, industrial-strength metals 3D printing, RFID, software-defined radio, early mobile phone architectures, open network server and switch/router architectures, and set-top box system design. At EBN Ann won two independently judged Editorial Excellence awards for Best Technology Feature. She holds a BA in Cultural Anthropology from Stanford University and a Certified Business Communicator certificate from the Business Marketing Association (formerly B/PAA).

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