SPACE/COSMOS

Newly discovered interstellar object 'may be oldest comet ever seen'

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Top view of the Milky Way galaxy showing the estimated orbits of both our Sun and the 3I/ATLAS comet. 3I/ATLAS is shown in red dashed lines, and the Sun is shown in yellow dotted lines. The large extent of 3I’s orbit into the outer thick disk is clear, while the Sun stays nearer the core of the galaxy. 

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Credit: M. Hopkins/Ōtautahi-Oxford team. Base map: ESA/Gaia/DPAC, Stefan Payne-Wardenaar, CC-BY-SA 4.0

Royal Astronomical Society press release

Friday 11 July 2025 

A mystery interstellar object discovered last week is likely to be the oldest comet ever seen – possibly predating our solar system by more than three billion years, researchers say.

The "water ice-rich" visitor, named 3I/ATLAS, is only the third known object from beyond our solar system ever spotted in our cosmic neighbourhood and the first to reach us from a completely different region of our Milky Way galaxy.

It could be more than seven billion years old, according to University of Oxford astronomer Matthew Hopkins – who is discussing his findings at the Royal Astronomical Society's National Astronomy Meeting 2025 in Durham – and may be the most remarkable interstellar visitor yet.

Unlike the previous two objects to enter our solar system from elsewhere in the cosmos, 3I/ATLAS appears to be travelling on a steep path through the galaxy, with a trajectory that suggests it originated from the Milky Way's 'thick disk' – a population of ancient stars orbiting above and below the thin plane where the Sun and most stars reside.

"All non-interstellar comets such as Halley's comet formed with our solar system, so are up to 4.5 billion years old," Hopkins said.

"But interstellar visitors have the potential to be far older, and of those known about so far our statistical method suggests that 3I/ATLAS is very likely to be the oldest comet we have ever seen."

The object was first spotted on 1 July 2025 by the ATLAS survey telescope in Chile, when it was about 670 million km from the Sun.

Hopkins' research predicts that, because 3I/ATLAS likely formed around an old, thick-disk star, it should be rich in water ice.

"This is an object from a part of the galaxy we've never seen up close before," said Professor Chris Lintott, co-author of the study and presenter of the BBC’s The Sky at Night.

"We think there's a two-thirds chance this comet is older than the solar system, and that it's been drifting through interstellar space ever since."

As it approaches the Sun, sunlight will heat 3I/ATLAS's surface and trigger cometary activity, or the outgassing of vapour and dust that creates a glowing coma and tail.

Early observations already suggest the comet is active, and possibly larger than either of its interstellar predecessors, 1I/'Oumuamua (spotted in 2017) and 2I/Borisov (2019).

If confirmed, this could have implications for how many similar objects future telescopes, such as the new Vera C. Rubin Observatory, are likely to detect. It may also provide clues about the role that ancient interstellar comets play in seeding star and planet formation across the galaxy.

"We're in an exciting time: 3I is already showing signs of activity. The gases that may be seen in the future as 3I is heated by the Sun will test our model," said co-author Dr Michele Bannister, of the University of Canterbury in New Zealand.

"Some of the biggest telescopes in the world are already observing this new interstellar object – one of them may be able to find out!"

The discovery of 3I caught the team by the surprise. It happened as they were gearing up for the beginning of survey operations with the Vera C. Rubin Observatory, which their model predicts will discover between 5 and 50 interstellar objects. 

"The solar system science community was already excited about the potential discoveries Rubin will make in the next 10 years, including an unprecedented number of interstellar objects," said co-researcher Dr Rosemary Dorsey, of the University of Helsinki.

"The discovery of 3I suggests that prospects for Rubin may now be more optimistic; we may find about 50 objects, of which some would be similar in size to 3I. This week's news, especially just after the Rubin First Look images, makes the upcoming start of observations all the more exciting."

The team's findings come from applying a model developed during Hopkins' doctoral research, which simulates the properties of interstellar objects based on their orbits and likely stellar origins.

Just a week before the comet's discovery, Hopkins had defended his thesis, and when 3I/ATLAS was announced, he was set to go on holiday. Instead, he found himself comparing real-time data to his predictions.

"Rather than the quiet Wednesday I had planned, I woke up to messages like '3I!!!!!!!!!!'," said Hopkins. "It's a fantastic opportunity to test our model on something brand new and possibly ancient."

The researchers' model, dubbed the Ōtautahi–Oxford Model, marks the first real-time application of predictive modelling to an interstellar comet.

For those keen to catch a glimpse of 3I/ATLAS, it should be visible through a reasonably-sized amateur telescope in late 2025 and early 2026.

ENDS

Images & video

3I/ATLAS - Figure 1

Caption: Top view of the Milky Way galaxy showing the estimated orbits of both our Sun and the 3I/ATLAS comet. 3I/ATLAS is shown in red dashed lines, and the Sun is shown in yellow dotted lines. The large extent of 3I’s orbit into the outer thick disk is clear, while the Sun stays nearer the core of the galaxy. 

Credit: M. Hopkins/Ōtautahi-Oxford team. Base map: ESA/Gaia/DPAC, Stefan Payne-Wardenaar, CC-BY-SA 4.0

 

3I/ATLAS - Figure 2

Caption: The same as Figure 1 with text labels showing the various arms of the galaxy, and the current meeting of our solar system and 3I/ATLAS in the Orion Arm towards the bottom.

Credit: M. Hopkins/Ōtautahi-Oxford team. Base map: ESA/Gaia/DPAC, Stefan Payne-Wardenaar, CC-BY-SA 4.0

 

3I/ATLAS - Figure 3

Caption: A zoomed-in version of Figure 1, the unlabelled orbits.

Credit: M. Hopkins/Ōtautahi-Oxford team. Base map: ESA/Gaia/DPAC, Stefan Payne-Wardenaar, CC-BY-SA 4.0

 

3I/ATLAS - Figure 4

Caption: A zoomed-in version of Figure 2, with text labels.

Credit: M. Hopkins/Ōtautahi-Oxford team. Base map: ESA/Gaia/DPAC, Stefan Payne-Wardenaar, CC-BY-SA 4.0

 

3I/ATLAS - Figure 5

Caption: A side-on view of the Milky Way, showing the estimated orbits of both our Sun and the 3I/ATLAS comet. 3I/ATLAS is shown in red dashed lines, and the Sun is shown in yellow dotted lines. The large extent of 3I’s orbit vertically into the outer thick disk is clear, while the Sun stays nearer the plane of the galaxy. 

Credit: M. Hopkins/Ōtautahi-Oxford team. Base map: ESA/Gaia/DPAC, Stefan Payne-Wardenaar, CC-BY-SA 4.0

 

3I/ATLAS - Figure 6

Caption: A zoomed-in version of Figure 5.

Credit: M. Hopkins/Ōtautahi-Oxford team. Base map: ESA/Gaia/DPAC, Stefan Payne-Wardenaar, CC-BY-SA 4.0

 

VLT timelapse of 3I/ATLAS

Caption: In this Very Large Telescope (VLT) timelapse, 3I/ATLAS is seen moving to the right over the course of about 13 minutes. These data were obtained with the FORS2 instrument on the VLT on the night of 3 July 2025, just two days after the comet was first discovered.

Credit: ESO/O. Hainaut

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Further information

The talk ‘The Galactic Interstellar Object Population in the LSST’ will take place at NAM at 10:00 BST on Friday 11 July 2025 in room TLC117. Find out more at: https://conference.astro.dur.ac.uk/event/7/contributions/751/

If you would like a Zoom link and password to watch it online, please email press@ras.ac.uk

Matthew Hopkins’ method uses the correlation between the ages and velocities of objects in the solar neighbourhood, specifically their vertical motion out of the plane of the galaxy. From an object's velocity, the method can put a confidence interval on its age.

The 68 per cent probability confidence interval calculated for 3I/ATLAS is 7.6–14 billion years.

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Notes for editors

The NAM 2025 conference is principally sponsored by the Royal Astronomical Society and Durham University.

 

About the Royal Astronomical Society

The Royal Astronomical Society (RAS), founded in 1820, encourages and promotes the study of astronomy, solar-system science, geophysics and closely related branches of science.

The RAS organises scientific meetings, publishes international research and review journals, recognises outstanding achievements by the award of medals and prizes, maintains an extensive library, supports education through grants and outreach activities and represents UK astronomy nationally and internationally. Its more than 4,000 members (Fellows), a third based overseas, include scientific researchers in universities, observatories and laboratories as well as historians of astronomy and others.

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The Science and Technology Facilities Council (STFC), part of UK Research and Innovation (UKRI), is the UK’s largest public funder of research into astronomy and astrophysics, particle and nuclear physics, and space science. We operate five national laboratories across the UK which, supported by a network of additional research facilities, increase our understanding of the world around us and develop innovative technologies in response to pressing scientific and societal issues. We also facilitate UK involvement in a number of international research activities including the ELT, CERN, the James Webb Space Telescope and the Square Kilometre Array Observatory.

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About Durham University 

Durham University is a globally outstanding centre of teaching and research based in historic Durham City in the UK. 

We are a collegiate university committed to inspiring our people to do outstanding things at Durham and in the world. 

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For more information about Durham University visit: www.durham.ac.uk/about/

 

Animal-inspired AI robot learns to navigate unfamiliar terrain

University of Leeds

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Robot adapts gait to recover from slips and trips on terrain including muddy grass and a pile of loose timber. 

Credit: Joseph Humphreys, University of Leeds

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Credit: Credit: Joseph Humphreys, University of Leeds

University of Leeds news  Friday, July 11, 2025 

WITH PICS & VIDEOS 

Researchers have developed an Artificial Intelligence (AI) system that enables a four-legged robot to adapt its gait to different, unfamiliar terrain, just like a real animal, in what is believed to be a world first. 

The pioneering technology allows the robot to change the way it moves autonomously, rather than having to be told when and how to alter its stride like the current generation of robots. This advance is seen as a major step towards potentially using legged robots in hazardous settings where humans might be put at risk, such as nuclear decommissioning or search and rescue, where the inability to adapt to the unknown could cost lives. 

For the study, conducted by the University of Leeds and University College London (UCL), the researchers took inspiration from the animal kingdom to teach the robot to navigate terrain that it had never seen before. This included four-legged animals such as dogs, cats and horses, which are adept at adjusting to different landscapes. These animals switch the way they move to save energy, maintain balance, or respond quickly to threats.  

The researchers have created a framework that can teach robots how to transition between trotting, running, bounding and more, just like mammals do in nature. 

Switching gaits when needed 

By embedding within the AI system the same strategies animals use to navigate an unpredictable world, the robot rapidly learns to switch gaits on the fly, in response to the terrain. Thanks to the data processing power of AI, the robot – nicknamed “Clarence”– learned the necessary strategies in just nine hours, considerably faster than the days or weeks most young animals take to confidently cross different surfaces. 

In a paper published today (July 11) in Nature Machine Intelligence, first author Joseph Humphreys, postgraduate researcher in the School of Mechanical Engineering at Leeds, explains how the framework enables the robot to change its stride in accordance with its environment, overcoming a variety of terrains including uneven timber, loose wood chips, and overgrown vegetation, without any alterations to the system itself.   

He said: “Our findings could have a significant impact on the future of legged robot motion control by reducing many of the previous limitations around adaptability.”  

He added: “This deep reinforcement learning framework teaches gait strategies and behaviour inspired by real animals – or ‘bio-inspired’ – such as saving energy, adjusting movements as needed, and gait memory, to achieve highly adaptable and optimal movement, even in environments never previously encountered. 

“All of the training happens in simulation. You train the policy on a computer, then take it and put it on the robot and it is just as proficient as in the training. It’s similar to the Matrix, when Neo's skill in martial arts is downloaded into his brain, but he doesn’t undergo any physical training in the real world. 

“We then tested the robot in the real-world, on surfaces it had never experienced before, and it successfully navigated them all. It was really rewarding to watch it adapt to all the challenges we set and seeing how the animal behaviour we had studied had become almost second nature for it.” 

Deep reinforcement learning agents are often good at learning a specific task but struggle to adapt when the environment changes. Animal brains have built-in structures and information that support learning. Some agents can imitate this kind of learning, but their artificial systems are usually not as advanced or complex. The researchers say they overcame this challenge by instilling their system with natural animal motion strategies. 

They say theirs is the first framework to simultaneously integrate all three critical components of animal locomotion into a reinforcement learning system—namely: gait transition strategies, gait procedural memory, and adaptive motion adjustment—enabling truly versatile, real-world deployment directly from simulation, without needing further adjustment on the physical robot 

In simple terms, the robot doesn’t just learn how to move — it learns how to decide which gait to use, when to switch, and how to adjust it in real time, even on terrain it has never encountered before.  

Professor Zhou, senior author of the study from UCL Computer Science, said: “This research was driven by a fundamental question: what if legged robots could move instinctively the way animals do? Instead of training robots for specific tasks, we wanted to give them the strategic intelligence animals use to adapt their gaits — using principles like balance, coordination, and energy efficiency. 

“By embedding those principles into an AI system, we’ve enabled robots to choose how to move based on real-time conditions, not pre-programmed rules. That means they can navigate unfamiliar environments safely and effectively, even those that they haven’t encountered before. 

“Our long-term vision is to develop embodied AI systems — including humanoid robots — that move, adapt, and interact with the same fluidity and resilience as animals and humans.” 

Real-world applications 

Engineers are increasingly imitating nature — known as biomimicry — to solve complex mobility challenges. The team say their achievement marks a major step forward in making legged robots more adaptable and capable of handling real-world challenges, in hazardous environments or where access is difficult. A robot capable of navigating unfamiliar, complex terrain opens up new possibilities for them to be used in disaster response, planetary exploration, agriculture and infrastructure inspection. 

It also suggests a promising pathway for integrating biological intelligence into robotic systems and conducting more ethical investigations of biomechanics hypotheses; instead of burdening animals with invasive sensors or putting them in danger to study their stability recovery response, robots can be used instead.  

By taking inspiration from factors that make animal movement effective, the researchers were able to develop a framework capable of traversing complex and high-risk terrain despite the robot not using exteroceptive sensors – those being sight, smell and hearing, that help humans in their movements. 

Parallel practice on multiple terrains 

Using deep reinforcement learning – effectively super-powered trial and error – the robot simultaneously practised within hundreds of environments, solving first the challenge of moving with different gaits then choosing the best gait for the terrain, generating the tools to achieve highly adaptable movement.  

To test this acquired adaptability in the real world, the robot was turned loose on real-life surfaces including woodchip, rocks, overgrown roots and loose timber, as well as having its legs repeatedly bashed by a sweeping brush, testing its ability to recover from trips. The team used a programmed route or a joystick – like those used in video games – to direct the robot. 

Perhaps surprisingly, the robot was not exposed to any rough terrain during training, highlighting the system's ability to adapt and demonstrating that these skills have become instinctive for the robot. 

The study, part-funded by the Royal Society and the Advanced Research and Invention Agency (ARIA), focused on enabling robust everyday movement. In future work, the team hope to add more dynamic skills, such as long-distance jumping, climbing, and navigating steep or vertical terrains. 

Although the framework has so far only been tested on a single dog-sized quadruped robot, the underlying principles are broadly applicable. The same bio-inspired metrics can be used across a wide range of four-legged robots, regardless of size or weight, as long as they share a similar morphology. 

Further Information 

The paper ‘Learning to Adapt through Bio-Inspired Gait Strategies for Versatile Quadruped Locomotion’ is published in Nature Machine Intelligence on Friday July 11, 2025. 

DOI: 10.1038/s42256-025-01065-z 

For media inquiries and interview requests please contact Deb Newman via d.newman@leeds.ac.uk and copy in pressoffice@leeds.ac.uk

Robot walks on concrete paving slabs.

Robot learning to adapt its gait to simulated terrain. It simultaneously practised within hundreds of simulated environments.

Analysing the AI system's gait selection policy [VIDEO] | 

Gait study: Comparing performance of the AI system’s gait selection policy to single gait usage, for all featured gaits, in terms of stability and efficiency. 

Credit: Joseph Humphreys, University of Leeds. 

Journal

Nature Machine Intelligence

DOI

10.1038/s42256-025-01065-z 

Method of Research

Computational simulation/modeling

Subject of Research

Not applicable

Article Title

Learning to Adapt through Bio-Inspired Gait Strategies for Versatile Quadruped Locomotion

Article Publication Date

11-Jul-2025

 

Pusan National University researchers identify key barriers hindering data-driven smart manufacturing adoption

A comprehensive set of issues, covering different aspects of manufacturing data analytics, can help manufacturers transition to smart manufacturing

Pusan National University

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CISM offers a structured guide for manufacturers to identify and resolve issues that emerge during MDA implementation, and also serves as a reference for developing educational and training resources. It has the potential to lead to wider adoption of MDA in the industry.

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Credit: Ki-Hun Kim from Pusan National University

Modern manufacturing operates in complex environments where traditional management approaches are no longer enough. This highlights the need for real-time, dynamic and self-adaptive management strategies. Manufacturing data analytics (MDA) has emerged as a powerful solution for transforming traditional manufacturing into smart manufacturing. Through MDA, manufacturers can identify hidden patterns in external and internal data, allowing them to better anticipate and respond to geopolitical risks and rapidly changing customer expectations and demands. However, despite these benefits, adoption of MDA remains surprisingly low, with fewer than one in five projects reaching full implementation.

Why the low uptake?

Because manufacturers face numerous challenges during MDA implementation. The MDA process typically involves five main steps: data preparation, data analysis, evaluation and interpretation of results, and implementation of results into manufacturing systems. Unique issues can arise at each of these steps. Additionally, there are broader issues related to technological, organizational, and environmental (TOE) contexts. While past studies have investigated many of these issues, they have not been able to provide a comprehensive list, overlooking TOE contexts, and being limited to only specific steps of MDA.

To address this gap, a research team led by Assistant Professor Ki-Hun Kim from the Department of Industrial Engineering at Pusan National University, South Korea, developed a comprehensive issue set for MDA implementation (CISM), with active contributions from Mr. Sa-Eun Park and Mr. Sang-Jae Lee, also from the same department. “To accelerate the adoption of MDA, manufacturers must first be able to identify and resolve the various challenges that may arise during its implementation. This includes not only technical issues but also organizational and environmental factors that significantly influence the success of MDA initiatives,” explains Dr. Kim. “CISM provides a structured framework to proactively recognize and resolve these issues, supporting a more efficient and effective MDA implementation.” The team included Mr. Sa-Eun Park and Mr. Sang-Jae Lee, also from Pusan National University. Their study was made available online on June 09, 2025, and published in Volume 82 of the Journal of Manufacturing Systems in October 2025.

The team first started by identifying relevant literature from the SCOPUS database. They identified 35 papers that addressed various issues related to MDA implementation. By systematically reviewing these papers, they identified a comprehensive set of 29 issues, grouped into 9 categories, each mapped to the relevant TOE context and step of the MDA process. Of these, 26 issues are related to technological context, 11 to organizational context, and 4 to environmental context.  The 9 categories of CISM reflect different aspects of the MDA process, from understanding the problem and preparing data, to identifying the knowledge gap between data scientists and domain experts, and aligning MDA models to real-world manufacturing systems.

To validate CISM, the research team applied it to three real-world case studies in the rubber manufacturing industry, focusing on optimizing recipe formulation and mixing processes to ensure consistent, high-quality production. The framework effectively captured all implementation challenges encountered during the projects, demonstrating its comprehensiveness and practical applicability.

The authors also highlight directions for future research: ranking the relative importance of each issue, exploring their relevance across different manufacturing contexts, and developing tailored strategies to address them.

“CISM can help manufacturers establish clear guidelines for identifying and prioritizing the issues that need to be proactively addressed to ensure effective MDA implementation,” notes Dr. Kim. “Moreover, it can serve as a foundational reference for developing education and training resources related to MDA. These efforts will, in turn, enable manufacturers to deliver high-quality products more efficiently and reliably, extending the benefits directly to consumers.”

In summary, CISM provides a solid foundation for both researchers and practitioners working to improve real-world MDA implementation. It represents a meaningful step forward in the journey toward widespread, data-driven smart manufacturing.

 

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Reference                                    

Title of original paper: Comprehensive issue identification for manufacturing data analytics implementation: Systematic literature review and case studies

Journal: Journal of Manufacturing Systems

DOI: 10.1016/j.jmsy.2025.05.006

                                    

About Pusan National University

Pusan National University, located in Busan, South Korea, was founded in 1946 and is now the No. 1 national university of South Korea in research and educational competency. The multi-campus university also has other smaller campuses in Yangsan, Miryang, and Ami. The university prides itself on the principles of truth, freedom, and service and has approximately 30,000 students, 1,200 professors, and 750 faculty members. The university comprises 14 colleges (schools) and one independent division, with 103 departments in all.

Website: https://www.pusan.ac.kr/eng/Main.do

About Assistant Professor Ki-Hun Kim

Dr. Ki-Hun Kim is an Assistant Professor in the Department of Industrial Engineering at Pusan National University and is jointly affiliated with the Graduate School of Data Science. His research focuses on the development and applications of AI for various industries such as manufacturing, logistics, and healthcare.

Lab: https://sites.google.com/view/iai-lab/home?authuser=0

Scopus ID: 57020115300

About Mr. Sa-Eun Park

Sa-Eun Park is currently a graduate student in the Department of Industrial Engineering at Pusan National University. He is pursuing an MS-Ph.D. integrated course under the supervision of Professor Ki-Hun Kim. His research involves developing advanced AI methods to optimize industrial processes and enhance decision-making capabilities.

About Mr. Sang-Jae Lee

Sang-Jae Lee earned a master’s degree in industrial engineering from Pusan National University. His research focuses on a time series forecasting and practical AI applications in various industries.

 

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Journal

Journal of Manufacturing Systems

DOI

10.1016/j.jmsy.2025.05.006 

Method of Research

Systematic review

Subject of Research

Not applicable

Article Title

Comprehensive issue identification for manufacturing data analytics implementation: Systematic literature review and case studies

Article Publication Date

1-Oct-2025