A few years ago, I was lucky enough to see the Bayeux Tapestry – the famous embroidered cloth depicting the Norman invasion of England in 1066. More than 900 years old, this historic piece of art is only 50 cm wide but more than 68 m long. It’s also extremely fragile, which why I was astonished when I heard that the Bayeux Tapestry was going to leave its home in Normandy and be transported across the English Channel to the UK.

The loan of the artwork, which was agreed between former British prime minister Keir Starmer and French president Emmanuel Macron last year, has now taken place. The tapestry arrived under police escort on 10 July and will be on display at the British Museum in London from September 2026 until July 2027. It is the first time the tapestry has been back in England, the country where this linen cloth was originally made.

I’m fascinated by how non-invasive optical techniques have been crucial in advancing our understanding of the tapestry

So what’s all this got to with physics? Well, I’m fascinated by how physics – and specifically, non-invasive optical techniques – have been crucial in advancing our understanding of the tapestry and its historical significance. Thanks to physics-based equipment developed by hi-tech manufacturers, we’ve learned about how the Bayeux Tapestry was initially made, what damage it subsequently suffered, how it was repaired, and the nature of its current condition.

Such information is vital not only for increasing our historical awareness, but also for current conservators who want to understand the tapestry’s underlying condition and find out how to preserve it for future generations. And before the pedants write in, yes I know that while it’s known as the Bayeux Tapestry, technically it’s an embroidery. That means it was created by stitching woollen threads onto a linen backing, which is helpful to know when reviewing the physics involved.

Optical sleuthing

The beauty of using optical methods to study the Bayeux Tapestry is that they are non-destructive and non-intrusive – and so can typically be applied in situ. In 2017 a three-year project was started, in which conservationists used high-resolution 2D imaging, photogrammetry, multimodal scanning and advanced image reconstruction techniques to determine the tapestry’s condition and conservation needs. The project revealed considerable information about how this artwork was created and modified over time.

Thanks to these high-resolution images, we now have a single digital map of the entire tapestry, making it possible to zoom in and look at individual threads. It’s yielded information such as the width of stitches and the positions where the wool threads cross the original linen background. As a result, historians now believe the tapestry was made by a single group of embroiderers, who – knowing the skills that existed at the time – were probably based in Canterbury, UK.

Techniques such as infrared spectroscopy and fluorescent and reflection ultraviolet imaging have revealed information about what’s underneath the surface of the tapestry

From the size and direction of the stitches, we also think the outlines of the figures were stitched first, with the shapes filled in afterwards using coloured wools in a strict, specific order. Meanwhile, techniques such as infrared spectroscopy and fluorescent and reflection ultraviolet imaging have revealed information about what’s underneath the surface of the tapestry. In particular, they’ve located the presence of stains such as glue, wax (dripped from candles that lit the tapestry) and even oil from the hands of people who touched it.

Infrared light passing through the dark-coloured wools have also revealed original sketch lines that were drawn on the linen base before the wool stitching started. It reveals the original artist’s draft, likely to be someone familiar with manuscript drawing – perhaps an illuminator of religious books.

3D imaging with photogrammetry

Researchers in France, led by Stéphane Le Mouélic from Nantes University, who originally did a PhD in astrophysics, have also used computer software to combine more than 440 overlapping 2D photos taken from multiple angles to create a detailed 3D model of the Bayeux Tapestry (J. Cult. Heritage 71 211).

It digitally “irons out” all the sags and wrinkles in the line fabric, recreating the original, “flat” tapestry and allowing the original physical shape of the artwork to be revealed for the first time. The 3D model can be viewed for free at the Bayeux Museum website, allowing historians, researchers and members of the public to study it without having to handle the delicate material.

Physics-based techniques have also allowed us to deal with the fact that parts of the fabric have been repaired over the last 900 plus years. Some of those repairs used non-authentic dyes and colours, while others covered up the original stitching. But using computer vision and image processing, threads that were added later can be subtracted digitally, providing a clearer, corrected view of what the original tapestry looked like.

Finally, optical tools can help keep the tapestry safe for future generations. Museum curators need to know if the cloth is sagging, tearing, or changing shape over time. Instead of putting heavy tools on the cloth, lasers can scan the cloth to accurately map of the contours of its surface. By repeating this exercise every few years, conservationists can spot if any part of the cloth has stretched or sagged, enabling minor problems to be rectified before they end up causing significant damage.

Without the huge amount of investigative and restorative work carried out, the Bayeux Tapestry would never have been allowed to make the journey to London

In 2025 Le Monde newspaper reported that experts had, over the previous five years, meticulously documented 24,204 stains, 9646 holes and 30 tears in the artwork. Without the huge amount of investigative and restorative work carried out, the Bayeux Tapestry would never have been allowed to make this year’s journey to London. Advanced and detailed optical measurement has “de-risked” the tapestry’s trip.

Tickets to the British Museum exhibition are reportedly being snapped up faster than those for Glastonbury. So if you don’t get to see the tapestry in person, feel free to marvel at this wonderful historical artwork by checking out the interactive online digital copy. It really is physics and history in the making.

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It was fantastic to see the men’s marathon world record being broken in London at the end of April when Kenya’s Sabastian Sawe became the first person to officially complete the run in under two hours. It’s an achievement that a whole generation of world-class runners had had their eyes on, with Sawe completing the race in a headline-grabbing 1:59:30.

Perhaps less attention was given to another achievement a week earlier, which saw a humanoid robot run the Beijing E-Town half-marathon in 50:26, breaking the human record by almost seven minutes. What’s particularly impressive is that at the inaugural event in 2025, the robot winner took 2:40:42 to complete the race, with many robot entrants failing even to reach the finishing line.

In just one year, in other words, the robot half-marathon record has been slashed by more than two-thirds, an unthinkable feat in human terms. What’s more, the rules between the first and second robotic competitions were tightened. They now favour robots that can run on their own (rather than via remote control) and penalize those that need to be taken off course to have their batteries changed.

Giant steps

My mind began spinning, trying to work out what must have happened over the past year to realize such advancements. How did we go from remote-controlled, slow and clunky robots to autonomous, agile and sleek devices? It turns out that these increases in speed and endurance have been driven by a combination of better hardware, specialized designs and advanced algorithms.

Robots now benefit from powerful actuators enabling higher torque and faster movement of its hips and knees. Anatomical designs have also been optimizied to mirror biological efficiencies by, for example, using lightweight limbs and lower torso components to minimize the energy lost when a robot’s foot strikes the ground.

Other changes include the use of liquid cooling technologies to stop a robot from getting too hot during prolonged activity and finding ways to mimic highly efficient natural bipeds, such as an emu or ostrich. Improved AI and control software have also allowed robots to navigate varied terrain and stay stable autonomously, rather than relying on remote operation.

AI answers

Of all these developments, I believe the most significant is the expanding use of AI. To me, it seems that AI will be the enabler for robots to become more “human”. In China, humanoid robots are already being developed to automate what is dubbed the hardest task in the car industry – the final assembly stage.

This work typically consists of real people carrying out highly labour-intensive tasks such as installing wiring harnesses, fitting a vehicle’s interior trim and instruments, or bringing its engine and chassis together. Being super-precise tasks that require manual dexterity, this work has previously been thought too complex for robots.

However, through a combination of imitation learning, simulation and real-time human guidance, it’s now becoming conceivable for robotic devices to do this kind of work. Underpinning this aspiration are what are known as  Vision-Language-Action (VLA) models, which process camera images together with natural language or text descriptions and translate these into physical actions.

Having emerged relatively recently from work at AI firm Google DeepMind, VLA models differ from traditional, task-specific programming robotics by offering generalization and the ability to handle novelty. They are seen as the next big thing in AI because they bridge the gap between understanding the world (vision and language) and interacting with it (action) – potentially letting robots do tasks they have not been explicitly trained on.

VLAs typically rely on existing Vision-Language-Models (VLMs), which provide a combined knowledge of text (large language models) and images (computer vision models). The VLA model is then fine-tuned on task-specific data to learn a mapping of visual observations and text instructions to create the desired robot action.

One challenge in training VLAs is getting enough data with appropriate visual and contextual information. It may be gleaned from real robots (imitation) but it can also be generated by human guidance or teleoperation – a manual process involving humans “showing” robots how to do a specific task. The task is time-consuming, having to be performed deliberately and repeatedly using equipment that captures appropriate levels of data quantity and quality.

A third option is to build virtual simulation environments to train the models and optimize robotic movements. As VLAs inherently learn to associate higher-level cognition with lower-level physical actions, the trend will be to move from fixed, pre-programmed scripts towards AI and large-scale simulation training. The result: robots that become more autonomous and versatile in real-world situations.

We are all getting used to AI becoming an increasing part of our everyday life – think how much GenerativeAI models have improved since they first appeared a few years ago. But I wonder what will be next for robots as they become capable of moving independently and spontaneously, having the ability to operate with full autonomy within dynamic human environments, such as homes and factories.

When we – eventually – experience the full physical embodiment of Artificial General Intelligence, will it be like living on the filmset of I, Robot or Blade Runner, with humans and humanoids coexisting and, in some cases indiscernible? Several years ago, this was something I did not expect to see in my lifetime. But in April it just got one giant leap – or at least one half-marathon – closer.

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