Lifeboat Foundation

Magnetically actuated soft robots may improve the treatment of disseminated intravascular coagulation. Significant progress has been made in the development of soft robotic systems that steer catheters. A more challenging task, however, is the development of systems that steer sub-millimeter-diameter guidewires during intravascular treatments; a novel microrobotic approach is required for steering. In this article, we develop a novel, magnetically actuated, soft microrobotic system, increasing the steerability of a conventional guidewire.

Soft RoboticsAhead of PrintFree AccessSungwoong Jeon, Ali Kafash Hoshiar, Kangho Kim, Seungmin Lee, Eunhee Kim, Sunkey Lee, Jin-young Kim, Bradley J. Nelson, Hyo-Jeong Cha, Byung-Ju Yi, and…

Robots performing automated tasks in uncontrolled environments need to adapt to environmental changes. Through building large collectives of robots, this robust and adaptive behavior can emerge from simple individual rules. These collectives can also be reconfigured, allowing for adaption to new tasks. Larger collectives are more robust and more capable, but the size of existing collectives is limited by the cost of individual units. In this article, we present a soft, modular robot that we have explicitly designed for manufacturability: Linbots use multifunctional voice coils to actuate linearly, to produce audio output, and to sense touch. When used in collectives, the Linbots can communicate with neighboring Linbots allowing for isolated behavior as well as the propagation of information throughout a collective. We demonstrate that these collectives of Linbots can perform complex tasks in a scalable distributed manner, and we show transport of objects by collective peristalsis and sorting of objects by a two-dimensional array of Linbots.

Soft RoboticsAhead of PrintFree AccessLinbots: Soft Modular Robots Utilizing Voice Coils Ross McKenzie, Mohammed E. Sayed, Markus P. Nemitz, Brian W. Flynn, and Adam A. Stokes Ross McKenzieScottish Microelectronics Centre, School of Engineering, Institute for Integrated Micro and Nano Systems,…

Increasing amounts of attention are being paid to the study of Soft Sensors and Soft Systems. Soft Robotic Systems require input from advances in the field of Soft Sensors. Soft sensors can help a soft robot to perceive and to act upon its immediate environment. The concept of integrating sensing capabilities into soft robotic systems is becoming increasingly important. One challenge is that most of the existing soft sensors have a requirement to be hardwired to power supplies or external data processing equipment. This requirement hinders the ability of a system designer to integrate soft sensors into soft robotic systems. In this article, we design, fabricate, and characterize a new soft sensor, which benefits from a combination of radio-frequency identification (RFID) tag design and microfluidic sensor fabrication technologies. We designed this sensor using the working principle of an RFID transporter antenna, but one whose resonant frequency changes in response to an applied strain. This new microfluidic sensor is intrinsically stretchable and can be reversibly strained. This sensor is a passive and wireless device, and as such, it does not require a power supply and is capable of transporting data without a wired connection. This strain sensor is best understood as an RFID tag antenna; it shows a resonant frequency change from approximately 860 to 800 MHz upon an applied strain change from 0% to 50%. Within the operating frequency, the sensor shows a standoff reading range of 7.5 m (at the resonant frequency). We characterize, experimentally, the electrical performance and the reliability of the fabrication process. We demonstrate a pneumatic soft robot that has four microfluidic sensors embedded in four of its legs, and we describe the implementation circuit to show that we can obtain movement information from the soft robot using our wireless soft sensors.

Soft RoboticsAhead of PrintFree AccessLijun Teng, Kewen Pan, Markus P. Nemitz, Rui Song, Zhirun Hu, and Adam A. Stokes Lijun TengThe School of Engineering, Institute for Integrated Mi…

The texture of an artist’s original work can now be reproduced with AI-controlled 3D printing.

Almost everything you hear about artificial intelligence today is thanks to deep learning. This category of algorithms works by using statistics to find patterns in data, and it has proved immensely powerful in mimicking human skills such as our ability to see and hear. To a very narrow extent, it can even emulate our ability to reason. These capabilities power Google’s search, Facebook’s news feed, and Netflix’s recommendation engine—and are transforming industries like health care and education.

Our study of 25 years of artificial-intelligence research suggests the era of deep learning is coming to an end.

The world has not entered the age of the killer robot, at least not yet. Today’s autonomous weapons are mostly static systems to shoot down incoming threats in self-defence, or missiles fired into narrowly defined areas. Almost all still have humans “in the loop” (eg, remotely pulling the trigger for a drone strike) or “on the loop” (ie, able to oversee and countermand an action). But tomorrow’s weapons will be able to travel farther from their human operators, move from one place to another and attack a wider range of targets with humans “out of the loop” (see article). Will they make war even more horrible? Will they threaten civilisation itself? It is time for states to think harder about how to control them.

A good approach is a Franco-German proposal that countries should share more information on how they assess new weapons; allow others to observe demonstrations of new systems; and agree on a code of conduct for their development and use. This will not end the horrors of war, or even halt autonomous weapons. But it is a realistic and sensible way forward. As weapons get cleverer, humans must keep up.

A movie montage for modern artificial intelligence might show a computer playing millions of games of chess or Go against itself to learn how to win. Now, researchers are exploring how the reinforcement learning technique that helped DeepMind’s AlphaZero conquer chess and Go could tackle an even more complex task—training a robotic knee to help amputees walk smoothly.

Computer algorithms help prosthetics wearers walk within minutes rather than requiring hours of training.

Law firms are under tremendous pressure to innovate to provide better value to their clients, who demand more value for their legal dollars. Providing higher-value services in turn boosts firms’ competitiveness.

However, much of the day-to-day work of any legal office – whether it’s in-house counsel, a boutique firm or one of the largest legal power houses – is the tedious, repetitive work of reading and preparing answers to complaints. Larger firms may have armies of junior associates do much of this necessary but mundane case-preparation work. At smaller firms, partners and senior associates are often involved in all stages of litigation. Preparing responses is time-consuming. It can take several hours to a full day to complete. Those are hours that both attorneys and firms would prefer to use tackling more strategic legal work.

We asked ourselves, what if, instead of taking hours, those high-volume, repetitive tasks could take a couple of minutes?

Continue reading “AI technology accelerates and augments legal work” »

A collaboration of researchers from MIT and Microsoft have developed a system that helps identify lapses in artificial intelligence knowledge in autonomous cars and robots. These lapses, referred to as “blind spots,” occur when there are significant differences between training examples and what a human would do in a certain situation — such as a driverless car not detecting the difference between a large white car and an ambulance with its sirens on, and thus not behaving appropriately.