A team of Facebook researchers has built a new kind of electronic skin and fingertip that offer an inexpensive, versatile, durable and replaceable solution for long-term robotic use.
Called ‘ReSkin’, it employs a self-supervised learning algorithm to help auto-calibrate the sensor, making it able to share data between sensors and systems to make robots more sensitive.
‘ReSkin’ is a new open source touch-sensing “skin” created by Meta AI researchers, in collaboration with Carnegie Mellon University, that can help researchers advance their AI’s tactile-sensing skills quickly and at scale, Facebook said in a statement late on Monday.
Robust tactile sensing is a significant bottleneck in robotics.
“Current sensors are either too expensive, offer poor resolution, or are simply too unwieldy for custom robots. ReSkin has the potential to overcome several of these issues,” said Lerrel Pinto, an assistant professor of computer science at New York University.
Its lightweight and small form factor makes it compatible with arbitrary grippers.
“We’ll be releasing the design, relevant documentation, code, and base models in order to help AI researchers use ReSkin without having to collect or train their own data sets. That, in turn, should help advance AI’s tactile sensing skills quickly and at scale,” Facebook noted.
A generalisable tactile sensing skin like aReSkin’ will provide a source of rich contact data that could be helpful in advancing AI in a wide range of touch-based tasks, including object classification, proprioception, and robotic grasping.
AI models trained with learned tactile sensing skills will be capable of many types of tasks, including those that require higher sensitivity, such as working in health care settings, or greater dexterity, such as maneuvering small, soft, or sensitive objects.