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You are on PRO Robots Channel, and today we present you with high-tech news. An exhibition of robot chefs in Japan and novelties from the robot exhibition Automate 2022 in the USA, new unusual robots for space, the unexpected discovery of a robot that visited the asteroid Bennu, and the first Italian humanoid robot. All the most interesting technology news in one issue!

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Scientists at the Lawrence Livermore National Laboratory (LLNL) Energetic Materials Center and Purdue University Materials Engineering Department have used simulations performed on the LLNL supercomputer Quartz to uncover a general mechanism that accelerates chemistry in detonating explosives critical to managing the nation’s nuclear stockpile. Their research is featured in the July 15 issue of the Journal of Physical Chemistry Letters.

Insensitive high explosives based on TATB (1,3,5-triamino-2,4,6-trinitrobenzene) offer enhanced safety properties over more conventional explosives, but physical explanations for these safety characteristics are not clear. Explosive initiation is understood to arise from hotspots that are formed when a shockwave interacts with microstructural defects such as pores. Ultrafast compression of pores leads to an intense localized spike in temperature, which accelerates chemical reactions needed to initiate burning and ultimately . Engineering models for insensitive high explosives—used to assess safety and performance—are based on the hotspot concept but have difficulty in describing a wide range of conditions, indicating missing physics in those models.

Using large-scale atomically resolved reactive molecular dynamics supercomputer simulations, the team aimed to directly compute how hotspots form and grow to better understand what causes them to react.

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Atoms are notoriously difficult to control. They zigzag like fireflies, tunnel out of the strongest containers and jitter even at temperatures near absolute zero.

Nonetheless, scientists need to trap and manipulate in order for , such as atomic clocks or quantum computers, to operate properly. If individual atoms can be corralled and controlled in large arrays, they can serve as quantum bits, or qubits—tiny discrete units of information whose state or orientation may eventually be used to carry out calculations at speeds far greater than the fastest supercomputer.

Researchers at the National Institute of Standards and Technology (NIST), together with collaborators from JILA—a joint institute of the University of Colorado and NIST in Boulder—have for the first time demonstrated that they can trap single atoms using a novel miniaturized version of “”—a system that grabs atoms using a laser beam as chopsticks.

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Colombia’s government has launched a partnership with Ripple Labs, the company behind the cryptocurrency XRP, to put land titles on the blockchain, part of a plan to rectify land distribution efforts so unfair they’ve led to decades of armed conflict.

The project, built by blockchain development company Peersyst Technology and Ripple, will permanently store and authenticate property titles on Ripple’s Ledger—its public blockchain.

This will help eliminate bureaucracy and hopefully make land distribution more equal, Ripple Labs and Peersyst Technology told Decrypt.

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Humans are good at looking at images and finding patterns or making comparisons. Look at a collection of dog photos, for example, and you can sort them by color, by ear size, by face shape, and so on. But could you compare them quantitatively? And perhaps more intriguingly, could a machine extract meaningful information from images that humans can’t?

Now a team of Standford University’s Chan Zuckerberg Biohub scientists has developed a machine learning method to quantitatively analyze and compare images—in this case microscopy images of proteins—with no prior knowledge. As reported in Nature Methods, their algorithm, dubbed “cytoself,” provides rich, detailed information on location and function within a cell. This capability could quicken research time for cell biologists and eventually be used to accelerate the process of drug discovery and drug screening.

“This is very exciting—we’re applying AI to a new kind of problem and still recovering everything that humans know, plus more,” said Loic Royer, co-corresponding author of the study. “In the future we could do this for different kinds of images. It opens up a lot of possibilities.”

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Researchers have been trying to build artificial synapses for years in the hope of getting close to the unrivaled computational performance of the human brain. A new approach has now managed to design ones that are 1,000 times smaller and 10,000 times faster than their biological counterparts.

Despite the runaway success of deep learning over the past decade, this brain-inspired approach to AI faces the challenge that it is running on hardware that bears little resemblance to real brains. This is a big part of the reason why a human brain weighing just three pounds can pick up new tasks in seconds using the same amount of power as a light bulb, while training the largest neural networks takes weeks, megawatt hours of electricity, and racks of specialized processors.

That’s prompting growing interest in efforts to redesign the underlying hardware AI runs on. The idea is that by building computer chips whose components act more like natural neurons and synapses, we might be able to approach the extreme space and energy efficiency of the human brain. The hope is that these so-called “neuromorphic” processors could be much better suited to running AI than today’s computer chips.

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In medicine, a prosthesis, or a prosthetic implant, is an artificial device that replaces a missing body part, which may be lost through trauma, disease, or a condition present at birth. A pioneering project to develop advanced pressure sensors for use in robotic systems could transform prosthetics and robotic limbs. The innovative research project aspires to develop sensors that provide enhanced capabilities to robots, helping improve their motor skills and dexterity, through the use of highly accurate pressure sensors that provide haptic feedback and distributed touch.

It is led by the University of the West of Scotland (UWS), Integrated Graphene Ltd, and supported by the Scottish Research Partnership in Engineering (SRPe) and the National Manufacturing Institute for Scotland (NMIS) Industry Doctorate Programme in Advanced Manufacturing. This is not for the first time when the team of highly talented researchers have decided to bring the much needed transformative change in prosthetics and robotic limbs.

The human brain relies on a constant stream of tactile information to carry out basic tasks, like holding a cup of coffee. Yet some of the most advanced motorized limbs — including those controlled solely by a person’s thoughts — don’t provide this sort of feedback. As a result, even state-of-the-art prosthetics can often frustrate their users.

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