While tactile sensing is widely accepted as an important and useful sensing modality, its use pales in comparison to other sensory modalities like vision and proprioception. AnySkin addresses the critical challenges that impede the use of tactile sensing -- versatility, replaceability, and data reusability. Building on the simplistic design of ReSkin, and decoupling the sensing electronics from the sensing interface, AnySkin simplifies integration making it as straightforward as putting on a phone case and connecting a charger. Furthermore, AnySkin is the first uncalibrated tactile-sensor with cross-instance generalizability of learned manipulation policies. To summarize, this work makes three key contributions: first, we introduce a streamlined fabrication process and a design tool for creating an adhesive-free, durable and easily replaceable magnetic tactile sensor; second, we characterize slip detection and policy learning with the AnySkin sensor; and third, we demonstrate zero-shot generalization of models trained on one instance of AnySkin to new instances, and compare it with popular existing tactile solutions like DIGIT and ReSkin.https://any-skin.github.io/

Robotic manipulation of cloth has applications ranging from fabrics manufacturing to handling blankets and laundry. Cloth manipulation is challenging for robots largely due to their high degrees of freedom, complex dynamics, and severe self-occlusions when in folded or crumpled configurations. Prior work on robotic manipulation of cloth relies primarily on vision sensors alone, which may pose challenges for fine-grained manipulation tasks such as grasping a desired number of cloth layers from a stack of cloth. In this paper, we propose to use tactile sensing for cloth manipulation; we attach a tactile sensor (ReSkin) to one of the two fingertips of a Franka robot and train a classifier to determine whether the robot is grasping a specific number of cloth layers. During test-time experiments, the robot uses this classifier as part of its policy to grasp one or two cloth layers using tactile feedback to determine suitable grasping points. Experimental results over 180 physical trials suggest that the proposed method outperforms baselines that do not use tactile feedback and has better generalization to unseen cloth compared to methods that use image classifiers. Code, data, and videos are available at https://sites.google.com/view/reskin-cloth.

Last week, Mark Zuckerberg officially announced that his company was changing its name from Facebook to Meta, with a prominent new focus on creating the metaverse. A defining feature of this metaverse will be creating a feeling of presence in the virtual world. Presence could mean simply interacting with other avatars and feeling like you are immersed in a foreign landscape. Or, it could even involve engineering some sort of haptic feedback for users when they touch or interact with objects in the virtual world. As part of all this, a division of Meta called Meta AI wants to help machines learn how humans touch and feel by using a robot finger sensor called DIGIT, and a robot skin called ReSkin.

Meta CEO Mark Zuckerberg announced Monday that the company has designed a new synthetic skin for robots that could enable the machines to help build the company's metaverse. A development collaboration with Carnegie Mellon University, ReSkin lets robots'feel' objects to know how much or little force should be used to perform tasks, such as gripping or moving small objects. The skin is up to three millimeters thick and can be used for more than 50,000 interactions, while also having a high temporal resolution of up to 400Hz and a spatial resolution of one millimeter with 90 percent accuracy. ReSkin is also inexpensive to produce, costing less than $6 each at 100 units and even less at larger quantities, Facebook AI shared in a blog post. Abhinav Gupta, a research scientist at Meta, said on a media call Friday robots that can feel will help the machines understand what humans are doing.

Learn more about what comes next. Facebook this morning announced ReSkin, an open source touch-sensing synthetic "skin" created by researchers at the company in collaboration with Carnegie Mellon University. Leveraging machine learning and magnetic sensing, ReSkin is designed to offer an inexpensive, versatile, durable, and replaceable solution for long-term use, employing an unsupervised learning algorithm to help auto-calibrate the sensor. Alongside ReSkin, and perhaps timed in effort to distract from exposes detailing its internal turbulence, Facebook also today outlined its broader progress in developing hardware, simulators, libraries, benchmarks, and datasets for touch sensing, which the company says form the foundation for AI systems that can understand and interact through touch. "We typically think of touch as a way to convey warmth and care, but it's also a key sensing modality for perceiving the world around us," Facebook research scientist Roberto Calandra and hardware engineer Mike Lambeta said in a blog post.

Without a sense of touch, Frankenstein's monster would never have realized that " fire bad" and we would have had an unstoppable reanimated killing machine on our hands. So be thankful for the most underappreciated of your five senses, one that robots may soon themselves enjoy. Facebook announced on Monday that it has developed a suite of tactile technologies that will impart a sense of touch into robots that the mad doctor could never imagine. But why is Facebook even bothering to look into robotics research at all? "Before I joined Facebook, I was chatting with Mark Zuckerberg, and I asked him, 'Is there any area related to AI that you think we shouldn't be working on?' Yann LeCun, Facebook's chief AI scientist recalled during a recent press call. "And he said, 'I can't find any good reason for us to work on robotics,' so that was the start of our FAIR [Facebook AI Research] research, that we're not going to work on robotics." "Then, after a few years," he continued, "it became clear that a lot of interesting progress in AI work is happening in the context of robotics because this is the nexus of where people in AI research are trying to get to; the full loop of perception, reasoning, planning and action, and then getting feedback from the from the environment." As such, FAIR has centered its tactile technology research on four main areas of study -- hardware, simulation, processing and perception. We've already seen FAIR's hardware efforts: the DIGIT, a " low-cost, compact high-resolution tactile sensor" that Facebook first announced in 2020. Unlike conventional tactile sensors, which typically rely on capacitive or resistive methods, DIGIT is actually vision-based. "Inside the sensors there is a camera, there are RGB LEDs placed around the silicon, and then there is a silicon gel," Facebook AI Research Scientist, Roberto Calandra, explained. "Whenever we touch the silicone on an object, this is going to create shadows or changes in color cues that are then recorded by the collar.

Soft sensors have continued growing interest in robotics, due to their ability to enable both passive conformal contact from the material properties and active contact data from the sensor properties. However, the same properties of conformal contact result in faster deterioration of soft sensors and larger variations in their response characteristics over time and across samples, inhibiting their ability to be long-lasting and replaceable. ReSkin is a tactile soft sensor that leverages machine learning and magnetic sensing to offer a low-cost, diverse and compact solution for long-term use. Magnetic sensing separates the electronic circuitry from the passive interface, making it easier to replace interfaces as they wear out while allowing for a wide variety of form factors. Machine learning allows us to learn sensor response models that are robust to variations across fabrication and time, and our self-supervised learning algorithm enables finer performance enhancement with small, inexpensive data collection procedures. We believe that ReSkin opens the door to more versatile, scalable and inexpensive tactile sensation modules than existing alternatives.