Abstract--T actile sensing holds great promise for enhancing manipulation precision and versatility, but its adoption in robotic hands remains limited due to high sensor costs, manufacturing and integration challenges, and difficulties in extracting expressive and reliable information from signals. In this work, we present a low-cost, easy-to-make, adaptable, and compact fingertip design for robotic hands that integrates multi-modal tactile sensors. We use strain gauge sensors to capture static forces and a contact microphone sensor to measure high-frequency vibrations during contact. These tactile sensors are integrated into a compact design with a minimal sensor footprint, and all sensors are internal to the fingertip and therefore not susceptible to direct wear and tear from interactions. From sensor characterization, we show that strain gauge sensors provide repeatable 2D planar force measurements in the 0-5 N range and the contact microphone sensor has the capability to distinguish contact material properties. We apply our design to three dexterous manipulation tasks that range from zero to full visual occlusion. Given the expressiveness and reliability of tactile sensor readings, we show that different tactile sensing modalities can be used flexibly in different stages of manipulation, solely or together with visual observations to achieve improved task performance. For instance, we can precisely count and unstack a desired number of paper cups from a stack with 100% success rate which is hard to achieve with vision only. More details and videos can be found in https://sites.google.com/view/tactilefingertip.

The manipulation of deformable linear flexures has a wide range of applications in industry, such as cable routing in automotive manufacturing and textile production. Cable routing, as a complex multi-stage robot manipulation scenario, is a challenging task for robot automation. Common parallel two-finger grippers have the risk of over-squeezing and over-tension when grasping and guiding cables. In this paper, a novel eagle-inspired fingernail is designed and mounted on the gripper fingers, which helps with cable grasping on planar surfaces and in-hand cable guiding operations. Then we present a single-grasp end-to-end 3D cable routing framework utilizing the proposed fingernails, instead of the common pick-and-place strategy. Continuous control is achieved to efficiently manipulate cables through vision-based state estimation of task configurations and offline trajectory planning based on motion primitives. We evaluate the effectiveness of the proposed framework with a variety of cables and channel slots, significantly outperforming the pick-and-place manipulation process under equivalent perceptual conditions. Our reconfigurable task setting and the proposed framework provide a reference for future cable routing manipulations in 3D space.

Grasp EveryThing (GET) Gripper . We demonstrate its capability in completing a variety of household tasks through teleoperation on the ALOHA system [1]. Abstract --We introduce the Grasp EveryThing (GET) gripper, a novel 1-DoF, 3-finger design for securely grasping objects of many shapes and sizes. Mounted on a standard parallel jaw actuator, the design features three narrow, tapered fingers arranged in a two-against-one configuration, where the two fingers converge into a V-shape. The GET gripper is more capable of conforming to object geometries and forming secure grasps than traditional designs with two flat fingers. Inspired by the principle of self-similarity, these V-shaped fingers enable secure grasping across a wide range of object sizes. Further to this end, fingers are parametrically designed for convenient resizing and interchangeability across robotic embodiments with a parallel jaw gripper . Additionally, we incorporate a rigid fingernail for ease in manipulating small objects. T actile sensing can be integrated into the standalone finger via an externally-mounted camera. A neural network was trained to estimate normal force from tactile images with an average validation error of 1.3 N across a diverse set of geometries. In grasping 15 objects and performing 3 tasks via teleoperation, the GET fingers consistently outperformed standard flat fingers. All finger designs, compatible with multiple robotic embodiments, both incorporating and lacking tactile sensing, are available on GitHub.

Abstract-- Dexterous manipulation, especially of small daily objects, continues to pose complex challenges in robotics. This paper introduces the DenseTact-Mini, an optical tactile sensor with a soft, rounded, smooth gel surface and compact design equipped with a synthetic fingernail. We propose three distinct grasping strategies: tap grasping using adhesion forces such as electrostatic and van der Waals, fingernail grasping leveraging rolling/sliding contact between the object and fingernail, and fingertip grasping with two soft fingertips. Through comprehensive evaluations, the DenseTact-Mini demonstrates a lifting success rate exceeding 90.2% when grasping various objects, spanning items from 1 mm basil seeds and small paperclips to items nearly 15mm. This work demonstrates the potential of soft optical tactile sensors for dexterous manipulation and grasping.

Antarctica's Thwaites Glacier is'holding on by its fingernails', experts say, after discovering that it has retreated twice as fast as previously thought over the past 200 years. The West Antarctica glacier – which is about the size of Florida – has been an important consideration for scientists trying to make predictions about global sea level rise. The potential impact of its retreat is huge because a total loss of Thwaites and its surrounding icy basins could raise global sea levels by up to 10 feet. That is why it is widely nicknamed the'Doomsday Glacier.' For the first time, scientists mapped in high-resolution a critical area of the seafloor in front of Thwaites that gives them a window into how fast the glacier has retreated and moved in the past.

Upon looking at photographs and drawing on their past experiences, humans can often perceive depth in pictures that are, themselves, perfectly flat. However, getting computers to do the same thing has proved quite challenging. The problem is difficult for several reasons, one being that information is inevitably lost when a scene that takes place in three dimensions is reduced to a two-dimensional (2D) representation. There are some well-established strategies for recovering 3D information from multiple 2D images, but they each have some limitations. A new approach called "virtual correspondence," which was developed by researchers at MIT and other institutions, can get around some of these shortcomings and succeed in cases where conventional methodology falters.

IBM has revealed the world's first 2 nanometer (nm) chip technology which can fit up to 50 billion transistors in an area the size of a fingernail, an advance the company claims can lead to "quadrupling cell phone battery life." According to the computing giant, the new breakthrough chip, revealed as a proof-of-concept on Thursday, can improve performance by 45 per cent over current 7nm semiconductors that are used in commercially available products. The company believes this will help meet the demands for increased chip performance and energy efficiency in the era of AI, and the Internet of Things. While initially, the computer chip industry used nanometres – hundreds of times thinner than a single human hair – to measure the physical size of transistors, the nm number has also found its use widely to describe new generations of the technology. Built on IBM's nanosheet technology, the current advance reportedly allows the company to fit up to 50 billion transistors on a chip the size of a fingernail, giving processor builders more space and options to infuse components for workloads like AI and cloud computing.

Imagine, for a moment, the simple act of picking up a playing card from a table. You have a couple of options: Maybe you jam your fingernail under it for leverage, or drag it over the edge of the table. Now imagine a robot trying to do the same thing. Tricky: Most robots don't have fingernails, or friction-facilitating fingerpads that perfectly mimic ours. So many of these delicate manipulations continue to escape robotic control.

This paper presents an acoustic sound recognizer to recognize what people are writing on a table or wall by utilizing the sound signal information generated from a key, pen, or fingernail moving along a textured surface. Sketching provides a natural modality to interact with text, and sound is an effective modality for distinguishing text. However, limited research has been conducted in this area. Our system uses a dynamic time- warping approach to recognize 26 hand-sketched characters (A-Z) solely through their acoustic signal. Our initial prototype system is user-dependent and relies on fixed stroke ordering. Our algorithm relied mainly on two features: mean amplitude and MFCCs (Mel-frequency cepstral coefficients). Our results showed over 80% recognition accuracy.