Abstract-- F orce estimation is the core indicator for evaluating the performance of tactile sensors, and it is also the key technical path to achieve precise force feedback mechanisms. This study proposes a design method for a visual tactile sensor (VBTS) that integrates a magnetic perception mechanism, and develops a new tactile sensor called MagicGel. The sensor uses strong magnetic particles as markers and captures magnetic field changes in real time through Hall sensors. On this basis, MagicGel achieves the coordinated optimization of multimodal perception capabilities: it not only has fast response characteristics, but also can perceive non-contact status information of home electronic products. I. INTRODUCTION With the rapid advancement of tactile sensor technology, its crucial role in robotics, automation systems, and human-computer interaction has become increasingly evident. Tactile sensors enhance a robot's ability to perceive its environment, equipping the robot with more precise and intelligent operational capabilities. In the field of flexible operation and human-computer interaction, accurate tactile perception is the key to realizing core functions such as bionic grasping and force-controlled interaction. Traditional tactile sensors are mostly based on piezoresistance, capacitance or piezoelectric principles, which can achieve quantitative force perception. However, they have significant limitations in spatial resolution, dynamic response range and force estimation accuracy. J Shan and J Zhao are co-first authors of the article.

Swarms of tiny robots guided by magnetic fields can coordinate to act like ants, from packing together to form a floating raft to lifting objects hundreds of times their weight. About the size of a grain of sand, the microrobots could someday do jobs larger bots cannot, such as unblocking blood vessels and delivering drugs to specific locations inside the human body. Sperm caught breaking Newton's third law of motion Jeong Jae Wie at Hanyang University in South Korea and his colleagues made the tiny, cube-shaped robots using a mould and epoxy resin embedded with magnetic alloy. These small magnetic particles enable the microrobots to be "programmed" to form various configurations after being exposed to strong magnetic fields from certain angles. The bots can then be controlled by external magnetic fields to perform spins or other motions.

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/

This work presents a reinforcement learning-based switching control mechanism to autonomously move a ferromagnetic object (representing a milliscale robot) around obstacles within a constrained environment in the presence of disturbances. This mechanism can be used to navigate objects (e.g., capsule endoscopy, swarms of drug particles) through complex environments when active control is a necessity but where direct manipulation can be hazardous. The proposed control scheme consists of a switching control architecture implemented by two sub-controllers. The first sub-controller is designed to employ the robot's inverse kinematic solutions to do an environment search for the to-be-carried ferromagnetic particle while being robust to disturbances. The second sub-controller uses a customized rainbow algorithm to control a robotic arm, i.e., the UR5 robot, to carry a ferromagnetic particle to a desired position through a constrained environment. For the customized Rainbow algorithm, Quantile Huber loss from the Implicit Quantile Networks (IQN) algorithm and ResNet are employed. The proposed controller is first trained and tested in a real-time physics simulation engine (PyBullet). Afterward, the trained controller is transferred to a UR5 robot to remotely transport a ferromagnetic particle in a real-world scenario, achieving a 98.86% success rate over 30 episodes for randomly generated trajectories, demonstrating the viability of the proposed approach for real-life applications. In addition, two classical pathfinding approaches, Attractor Dynamics and the execution extended Rapidly-Exploring Random Trees (ERRT), are also investigated and compared to the RL-based method. The proposed RL-based algorithm is shown to achieve performance comparable to that of the tested classical path planners whilst being more robust to deploy in dynamical environments.

In the 1991 film'Terminator 2: Judgement Day' T-1000 liquifies himself to walk through metal bars, and this sci-fi scene is recreated in a real-world robot. A video of a shape-shifting robot shows it trapped in a cage, melting and then sliding through the bars where it reforms on the outside. Researchers led by The Chinese University of Hong Kong created the new phase-shifting material by embedding magnetic particles in gallium, a metal with a very low melting point of 85 degrees Fahrenheit. While the team does not see the innovation threatening humanity like in the Terminator movie, they foresee it removing foreign objects from the body or delivering drugs on demand. Scientists tested the robot through a series of'obstacles.'

A high-tech scanner that detects light deep inside the brain has been developed. It could boost cancer treatments and AI (artificial intelligence) and even lead to a screening program for Alzheimer's disease. The optical approach uses MRI (magnetic resonance imaging) to map how light spreads in opaque environments capturing dynamic changes in colors of tissue. It could map neuron-stimulating fibers during experiments or monitor patients receiving light-based therapies for tumors. "We can image the distribution of light in tissue, and that's important because people who use light to stimulate tissue or to measure from tissue often don't quite know where the light is going, where they're stimulating, or where the light is coming from. Our tool can be used to address those unknowns," says senior author Alan Jasanoff, a professor of biological engineering, brain and cognitive sciences, as well as nuclear science and engineering, at MIT, in a statement.

Researchers at The Chinese University of Hong Kong have created a "soft robot" made of slime containing magnetic particles, which can be manipulated using external magnets. The magnetic particles are toxic, but have theoretically been made safe to enter the human body after being covered in a layer of silicone compound - although further safety testing will be needed in the future. The team in Hong Kong hope the slime will one day be used to collect objects which have been accidentally swallowed. You can read more about the team's research here. This video has no sound.

A bizarre'magnetic tentacle robot' that can pass into the narrow tubes of the lungs to take tissue samples could help save lives, a new study shows. Experts at the University of Leeds have created the device, which consists of external magnets and a'tentacle' – a thin polymer tube containing metallic particles. The so-called'tentacle' is highly flexible and measures just 0.07 of an inch (2 mm) in diameter, about twice the size of the tip of a ballpoint pen. Like something from a horror film, the tentacle would slowly enter the mouth or nose of a patient while they are under general anaesthetic. Guided by the external magnets, it could reach some of the smallest bronchial tubes in the lungs – and could be used to take tissue samples or deliver cancer therapy.

A thin, replaceable skin that allows robots to "feel" could help in the construction of the metaverse, the proposed virtual future of the internet being developed by Meta (formerly Facebook) and others. The skin, jointly developed by Meta and Carnegie Mellon University in Pennsylvania, combines a rubbery plastic less than 3 millimetres thick and studded with magnetic particles with an artificial intelligence to calibrate its sense of touch. "If you look at how AI has advanced, we've made huge advances in computer vision and sound," says Abhinav Gupta at Meta AI Research. "But conspicuously, touch has been missing from this advancement." When the skin touches a surface, the plastic deforms and alters the magnetic field created by the embedded particles.

A magnetic spray is capable to turning objects into moving robots, which could be used to navigate drugs throughout the body. Scientists at the City University in Hong Kong revealed the innovation made of polyvinyl alcohol, gluten and iron particles. Called'M-spray,' it is capable of sticking on the targeted object and when it activates, allows the object to walk, roll and crawl using a magnetic field. The team foresees their creation being applied to pills, which doctors to move to a targeted part of the body. A magnetic spray is capable to turning objects into moving robots, which could be used to navigate drugs throughout the body.