How Diligent’s Robots Are Making a Difference in Texas Hospitals

For the last several years, Diligent Robotics has been testing out its robot, Moxi, in hospitals in Texas. Diligent isn’t the only company working on hospital robots, but Moxi is unique in that it’s doing commercial mobile manipulation, picking supplies out of supply closets and delivering them to patient rooms, all completely autonomously. A few weeks ago, Diligent announced US $10 million in new funding, which comes at a critical time, as the company addressed in their press release: Now more than ever hospitals are under enormous stress, and the people bearing the most risk in this pandemic are the nurses and clinicians at the frontlines of patient care. Our mission with Moxi has always been focused on relieving tasks from nurses, giving them more time to focus on patients, and today that mission has a newfound meaning and purpose. Time and again, we hear from our hospital partners that Moxi Continue reading How Diligent’s Robots Are Making a Difference in Texas Hospitals

How robots can help combat COVID-19

Can robots be effective tools in combating the COVID-19 pandemic? A group of leaders in the field of robotics say yes, and outline a number of examples. They say robots can be used for clinical care such as telemedicine and decontamination; logistics such as delivery and handling of contaminated waste; and reconnaissance such as monitoring compliance with voluntary quarantines. More details

Video Friday: Qoobo the Headless Robot Cat Is Back

Video Friday is your weekly selection of awesome robotics videos, collected by your Automaton bloggers. We’ll also be posting a weekly calendar of upcoming robotics events for the next few months; here’s what we have so far (send us your events!): ICARSC 2020 – April 15-17, 2020 – [Online Conference] ICRA 2020 – May 31-4, 2020 – [TBD] ICUAS 2020 – June 9-12, 2020 – Athens, Greece RSS 2020 – July 12-16, 2020 – [Online Conference] CLAWAR 2020 – August 24-26, 2020 – Moscow, Russia Let us know if you have suggestions for next week, and enjoy today’s videos.

Neural networks facilitate optimization in the search for new materials

When searching through theoretical lists of possible new materials for particular applications, such as batteries or other energy-related devices, there are often millions of potential materials that could be considered, and multiple criteria that need to be met and optimized at once. Now, researchers at MIT have found a way to dramatically streamline the discovery process, using a machine learning system. As a demonstration, the team arrived at a set of the eight most promising materials, out of nearly 3 million candidates, for an energy storage system called a flow battery. This culling process would have taken 50 years by conventional analytical methods, they say, but they accomplished it in five weeks. The findings are reported in the journal ACS Central Science, in a paper by MIT professor of chemical engineering Heather Kulik, Jon Paul Janet PhD ’19, Sahasrajit Ramesh, and graduate student Chenru Duan. The study looked at a Continue reading Neural networks facilitate optimization in the search for new materials

Putting artificial intelligence to work in the lab

Scientists have demonstrated fully-autonomous SPM operation, applying artificial intelligence and deep learning to remove the need for constant human supervision. The new system, dubbed DeepSPM, bridges the gap between nanoscience, automation and artificial intelligence (AI), and firmly establishes the use of machine learning for experimental scientific research using Scanning Probe Microscopy (SPM). More details

Google Invents AI That Learns a Key Part of Chip Design

There’s been a lot of intense and well-funded work developing chips that are specially designed to perform AI algorithms faster and more efficiently. The trouble is that it takes years to design a chip, and the universe of machine learning algorithms moves a lot faster than that. Ideally you want a chip that’s optimized to do today’s AI, not the AI of two to five years ago. Google’s solution: have an AI design the AI chip. “We believe that it is AI itself that will provide the means to shorten the chip design cycle, creating a symbiotic relationship between hardware and AI, with each fueling advances in the other,” they write in a paper describing the work that posted today to Arxiv. “We have already seen that there are algorithms or neural network architectures that… don’t perform as well on existing generations of accelerators, because the accelerators were designed like Continue reading Google Invents AI That Learns a Key Part of Chip Design

System trains driverless cars in simulation before they hit the road

A simulation system invented at MIT to train driverless cars creates a photorealistic world with infinite steering possibilities, helping the cars learn to navigate a host of worse-case scenarios before cruising down real streets.   Control systems, or “controllers,” for autonomous vehicles largely rely on real-world datasets of driving trajectories from human drivers. From these data, they learn how to emulate safe steering controls in a variety of situations. But real-world data from hazardous “edge cases,” such as nearly crashing or being forced off the road or into other lanes, are — fortunately — rare. Some computer programs, called “simulation engines,” aim to imitate these situations by rendering detailed virtual roads to help train the controllers to recover. But the learned control from simulation has never been shown to transfer to reality on a full-scale vehicle. The MIT researchers tackle the problem with their photorealistic simulator, called Virtual Image Synthesis Continue reading System trains driverless cars in simulation before they hit the road