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Boston Dynamics has unveiled the latest version of its Atlas Robot that is capable of improved human-like movement and actions. The Waltham, Massachusetts-based robot firm, showed off the humanoid robot with grippers while displaying its capabilities via a video posted to YouTube. The robot is seen performing complex actions such as grabbing and throwing an object, navigating complex terrain while holding another object.

In the video posted on Boston Dynamics’ YouTube channel, the company’s Atlas Robot is seen navigating a challenging environment while walking on two legs mimicking human motion. The Atlas Robot is also seen grabbing hold of a plank and jumping to spin around, carrying it and placing it to create a bridge to walk between two platforms, all while holding onto a toolkit.

The Atlas Robot is then seen walking across the platform bridge it created, tossing up the toolkit to the person on top of the scaffolding. The executed manoeuvre is being referred to by Boston Dynamics as an inverted 540-degree, multi-axis flip.

After successfully passing the toolkit along to the human on the ledge above, the Atlas Robot then theatrically proceeds to do a perfectly executed backflip while still on the thin platform.

The demonstration shows how humanoid robots such as the Atlas could potentially replace humans in performing tasks that involve risk of life and injury when performed by humans.

Recently, Boston Dynamics and IBM were seen collaborating to deploy robot dogs at the US National Grid sites for the purpose of autonomous inspection. The robotic dogs named Spot, are integrated by artificial intelligence (AI) developed by IBM Research and being deployed at the electric and gas utility sites in Massachusetts and New York for regular inspections.

However, Boston Dynamics’ creative foray into deploying robots in real-life situations and applications hasn’t always been smooth. The company faced backlash for selling Spot to local police departments, including the NYPD, leading to the NYPD cancelling the arrangement.


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Earth’s Spin to Speed Up Briefly, Causing Shorter Days This Summer

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Earth’s Spin to Speed Up Briefly, Causing Shorter Days This Summer

Reports indicate that for three days this summer – July 9, July 22 and August 5 – Earth’s rotation will speed up slightly, trimming 1.3 to 1.5 milliseconds off each day. Imperceptible in everyday life, this shift underscores how the Moon’s position influences our planet’s spin. For reference, the shortest day on record was July 5, 2024, lasting 1.66 milliseconds less than 24 hours. Over billions of years Earth’s rotation has slowly lengthened, but recent data show speedups. Scientists say monitoring these tiny changes is important for understanding Earth’s dynamics and timekeeping.

Causes of Faster Spin

According to timeanddate.com, the shortest-ever recorded day was on July 5, 2024, which was 1.66 milliseconds shy of 24 hours. The acceleration is largely driven by the Moon’s gravity. On those dates (July 9, July 22 and August 5), the Moon will lie far north or south of Earth’s equator, weakening its tidal braking on our planet’s spin. As a result, Earth rotates a bit faster – like spinning a top held at its ends. Seasonal shifts in mass distribution also affect rotation. Richard Holme of the University of Liverpool notes that summer growth and melting snow in the Northern Hemisphere move mass outward from Earth’s axis, slowing the spin in the same way an ice skater slows by extending her arms.

Timekeeping and Technology

Shifts in day length are handled by precise timekeeping. The International Earth Rotation and Reference Systems Service (IERS) monitors Earth’s spin and adds leap seconds to keep Coordinated Universal Time (UTC) in sync with solar time. Normally a second is added when Earth’s rotation slows, but if the spin-up trend continues, scientists have floated a “negative leap second” – removing a second – to realign clocks.

Dr. Michael Wouters of Australia’s National Measurement Institute says this fix would be unprecedented, and notes that even if a few seconds accumulated over decades, it would likely go unnoticed. Dr. David Gozzard of the University of Western Australia points out that GPS satellites, communications networks and power grids rely on atomic clocks synced to nanoseconds, and that millisecond-scale changes in Earth’s rotation are easily absorbed by these systems.

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James Webb Telescope Spots Rare ‘Cosmic Owl’ Formed by Colliding Galaxies

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James Webb Telescope Spots Rare ‘Cosmic Owl’ Formed by Colliding Galaxies

NASA’s James Webb Space Telescope has captured the “Cosmic Owl,” a startling owl-faced pair of colliding ring galaxies. This double-ring structure is exceptionally rare: ring galaxies account for just 0.01% of known galaxies, and two colliding rings is almost unheard of. The JWST image provides an exceptional natural laboratory for studying galaxy evolution. Models suggest the galactic clash began roughly 38 million years ago, meaning the owl-like shape could persist for a long time. A team led by Ph.D. student Mingyu Li of Tsinghua University in China announced the finding.

Spotting the ‘Cosmic Owl’

According to Mingyu Li, the first author of the new study , he and his team found the Owl by combing through public JWST data from the COSMOS field. The twin ring galaxies jumped out thanks to JWST’s infrared imaging. Each ring is about 26,000 light-years across (a quarter of the Milky Way), and each harbors a supermassive black hole at its core – one of the Owl’s eyes.

JWST images show the collision interface – the Owl’s beak – ablaze with activity. ALMA observations find a huge clump of molecular gas there – the raw fuel for new stars – being squeezed by the impact. Radio observations show a jet from one galaxy’s black hole slamming into the gas. Li notes the shockwave-plus-jet have ignited an intense starburst, turning the beak into a stellar nursery.

Rarity and Significance

Ring galaxies are extremely rare (≈0.01% of all galaxies), so finding two in collision is unheard of. Another team independently identified the same system and called it the “Infinity Galaxy”. Li says this event is an exceptional natural laboratory for studying galaxy evolution. In one view, researchers can see black holes feeding, gas compressing and starbursts happening together.

Li points out the collision’s shockwave and jet have triggered an intense starburst in the beak. He says this may be a crucial way to turn gas into stars rapidly, which could help explain how young galaxies built up their mass so quickly. Simulations will clarify the precise collision conditions needed to produce such a rare twin-ring “owl” shape.

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MIT Develops Low-Resource AI System to Control Soft Robots with Just One Image

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MIT Develops Low-Resource AI System to Control Soft Robots with Just One Image

The use of conventional robots for industry and hazardous environments is easy for the purpose of control and modelling. However, these are too rigid to operate in confined places and uneven terrain. The soft bio-related roots are better adapted to the environment and manoeuvring in inaccessible places. Such flexible capabilities would need an array of on-board sensors and spacious models which are tailored to each robot design. Having a new and less resource-demanding approach, the researchers at MIT have developed a far less complex, deep learning control system that teaches the soft, bio-inspired robots to follow the command from a single image only.

Soft Robots Learn from a Single Image

As per Phys.org, this research has been published in the journal Nature, by training a deep neural network on two to three hours of multi-view images of various robots executing random commands, the scientists trained the network to reconstruct the range and shape of mobility from only one image. The previous machine learning control designs need customised and costly motion systems. Lack of a general-purpose control system limited the applications and made prototyping less practical.

The methods unshackle the robotics hardware design from the ability to model it manually. This has dictated precision manufacturing, extensive sensing capabilities, costly materials and reliance on conventional and rigid building blocks.

AI Cuts Costly Sensors and Complex Models

The single camera machine learning approach allows the high-precision control in tests on a variety of robotic systems, adding the 3D-printed pneumatic hand, 16-DOF Allegro hand, a soft auxetic wrist and a low-cost Poppy robot arm.

As this system depends on the vision alone, it might not be suitable for more nimble tasks which need contact sensing and tactile dynamics. The performance may also degrade in cases where visual cues are not enough.

Researchers suggest the addition of sensors and tactile materials that can enable the robots to perform different and complex tasks. There is also potential to automate the control of a wider range of robots, together with minimal or no embedded sensors.

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