This article looks at where those areas tend to be, what the international register of certified robots actually contains, and the five questions a plant should settle before a robot goes in.

By Wai-keung Fung, Cardiff Metropolitan University

At the recent World Humanoid Robot Games 2026 in Beijing, robots sprinted, boxed, played football and tackled tasks inspired by factories, restaurants and emergency situations. The robot Tiangong Ultra even completed the 100 metres in 8.64 seconds, quicker than Usain Bolt’s 9.58-second world record.

But if we really want to know whether humanoid robots are ready for everyday life, a far less glamorous challenge may tell us more: doing the laundry. Can a robot take a crumpled T-shirt from a laundry basket, work out which way round it is, fold it neatly and recover when the sleeve slips from its hand?

No robot system can yet reliably handle the whole laundry process, from carrying a basket to the washing machine and sorting clothes for different programmes to taking them out, hanging them to dry, folding and ironing them. Even folding remains a challenge: robots need to cope with clothes of different shapes, sizes, materials and levels of creasing.

Running fast is difficult, of course. A humanoid has to coordinate many joints, keep its balance and generate powerful movements without falling over. The task itself, however, is fairly clear. The robot knows where to go, the track is predictable and the finish line does not suddenly change shape.

Laundry is a very different problem. A shirt may be inside out. One sleeve may be hidden underneath another garment. A pair of trousers may be twisted around a towel. Fabric is also soft and constantly changing shape. Pick up one corner of a T-shirt and the rest of it moves.

Humans deal with this without giving it much thought. We shake a shirt out, notice where the sleeves are, feel when the fabric slips and adjust our grip almost automatically. For a robot, every one of those little actions is a problem to solve. This is why an everyday chore can reveal more about robot intelligence than a spectacular athletic feat.

A useful household robot cannot simply replay the same movement again and again. It has to notice when reality is different from what it expected and adapt. That’s important to consider because humanoid robots are increasingly discussed as future helpers in homes and care settings, as well as factories and warehouses.

A factory production line can often be arranged so that parts arrive in predictable positions. A family home is not like that: clothes, toys and furniture move around constantly, while people, children and pets make the environment even less predictable.

Why one robot cannot simply copy another

There is another difficulty. Once one robot learns how to fold a shirt, it might seem obvious that we should simply transfer that skill to every other robot. Unfortunately, physical skills do not work like an app that can be installed on any machine.

Imagine a specialist laundry robot with rigid arms, overhead cameras and grippers designed specifically for fabric. A humanoid may have cameras in its head, five-fingered hands and different joints and balance constraints. The same movement could therefore produce a very different result.

A fixed robot arm can reach forward without worrying about falling over. A humanoid may have to move its hips, legs and feet just to keep itself stable while reaching across a table.

Sensors are important too. One robot may have sensitive touch sensors in its fingers. Another may rely mainly on cameras.

Some knowledge can still transfer. One robot can teach another something about what a shirt looks like, where its shoulders are likely to be, what a good fold looks like or how to recognise when a sleeve is trapped underneath. The receiving robot still has to work out how to carry out those ideas with its own hands, joints, cameras and body.

Research projects such as Open X-Embodiment are exploring exactly this problem by pooling experience from many different robots. The project has brought together more than one million real robot trajectories from 22 different robot types. The hope is that robots can learn useful patterns from one another rather than starting from zero every time.

What should count as progress?

The Humanoid Robot Games are valuable because they make robotics visible and show how quickly the technology is moving.

We should simply be careful about what those demonstrations prove. For robots intended to live and work alongside us, better questions might be: can the robot deal with an unfamiliar object? Can it notice that it has made a mistake and recover without human intervention? How long can it keep working before someone has to step in?

These measures are unlikely to produce viral videos. They are, however, much closer to the abilities that will determine whether humanoid robots become genuinely useful.

A robot that can run 100 metres faster than the human world record time is impressive. A robot that can take a messy basket of laundry, sort it, fold it and recognise when things have gone wrong may tell us far more about whether general purpose robot intelligence has really arrived.

The most important race for humanoid robots may not be against the clock. It may be against the laundry pile.

Wai-keung Fung, Senior Lecturer in Electronics Robotics & Control Engineering, Cardiff Metropolitan University

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Virtual Dead Heat

A new study finds U.S. AI is only 3% better than top alternatives from China.

That ranking comes from Bloomberg Analyst Robert Lea, who says AI from China shrank the gap to 3% with the release of DeepSeek V4.1 Flash.

Observes the Business Times: “China’s ascent is a result of deepening AI expertise and its researchers’ ability to optimize their models for domestic hardware.”

In other news and analysis on AI writing:

*Chinese AI Phenom DeepSeek on Track for $12 Billion in New Funding: Nearly- as-good-as-U.S.-AI -maker DeepSeek appears certain to raise at least another $12 billion as it readies to launch as an IPO in early 2027.

DeepSeek has been a hit with many U.S. firms, which prefer Open Source AI that often costs pennies-on-the-dollar compared to AI from OpenAI, Anthropic, Google and the like.

Observes writer Cris Tolomia: “At investor meetings this year, DeepSeek founder Liang Wenfeng committed to keeping DeepSeek’s AI models open and said the company’s overarching aim is advancing toward artificial general intelligence.”

*ChatGPT-6 – OpenAI’s Smartest Model – Now Available for Free Users: People who don’t pay a cent for AI can still get a taste of OpenAI’s smartest AI for free.

Observes the OpenAI Blog: “We’re bringing that next generation of intelligence to more people with a new GPT‑6 model built for more than 1.2 billion people who use ChatGPT each week.”

*ChatGPT-6 Answers Get Prettier: Look for More Graphics, Photos, Buttons, Forms, Charts and Interactive Experiences: Apparently, it’s not enough for bleeding edge AI to reel-out often whip-smart answers to users’ questions anymore – you need to do it in style.

Consequently, ChatGPT-maker OpenAI has stepped-up its game on pretty and is promising to respond with all sorts of multimedia in its responses to users.

Observes the OpenAI Blog: The new initiative has upgraded ChatGPT to “answer quickly with fully interactive user interfaces that make everyday answers more visual, make learning complex topics easier — and that can quickly create a tool to solve a task right in the moment.”

*Gemini Gets Dumber for Some Users: People on free or nearly free Gemini subscriptions have been downgraded.

Google has announced that users on Gemini free plans, for example, will only have access to Gemini Flash-Lite 3.5.

Moreover, users on the $5/month Google AI Plus plan have lost access to Gemini 3.1 Pro – although they can still use Gemini 3.6 Flash.

*Gemini Upgrade Gets Limited Release: As has been the trend with upgrades from AI titans, Google has decided on a limited roll-out of its new Gemini upgrade – Gemini 4 — due to security concerns.

Observes writer Kate Conger: “Google’s new model will initially be available only to certain companies and organizations focused on cybersecurity defense.

“It will be released more widely after defenders have had a chance to patch their systems and remedy bugs that the model uncovers.”

*New Gemini AI Agent Can Work Independently for Days: Google is out with an enhanced AI agent that can work unsupervised for days — and comes with its own Gmail, Calendar and Google Drive storage.

Observes writer Carl Franzen: “Google is unveiling a new universal AI agent for the workplace that can research information, create documents, write code, coordinate other AI agents and complete tasks lasting hours or days — all while maintaining the same memory, business context and identity across different applications and devices.”

So far, the beefed-up agent is only available to select customers – with the promise of wider release down-the-line.

*AI Agents in Crosshairs of Federal Trade Commission: Disturbed by the unpredictability of AI agents, the FTC has launched an investigation into the risk associated with AI agents from firms like ChatGPT-maker OpenAI and Anthropic.

Marketed as a panacea for AI users enthralled by the promise of AI assistants that can work independently on tasks for hours or even days, AI agents have also been known to make unexpected decisions – including bad decisions that often cost consumers serious money.

Observes writer Aminu Abdullahi: “The FTC plans to demand company documents and seek testimony from executives.”

*Insurers Brace for Millions in Claims Against AI Agents: The finicky nature of AI agents – often leading to unexpected and sometimes costly outcomes – is almost certain to trigger an onslaught of liability claims, according to writer Manuel Uth.

Observes Uth: “Tim Rayner, head of underwriting and claims at Verisk, says the buck stops with the CEO.

“Insurance broker Aon has reviewed over 300 AI-related legal cases and flagged risks lurking in cybersecurity, intellectual property and tech failure policies.”

*U.S. Doubles-Down on AI: Appoints New AI Czar With Clear Accountability: New AI head honcho Jay Clayton is steering a new effort to conduct a complete analysis of the impact of AI on the U.S. – with a report due date for early January 2027.

Observes writer Alex Leary: “The task force will still yield to the industry as the primary vehicle for managing risks, officials said.

“But it will work in conjunction with the industry to identify risk and opportunities.”

Share a Link:  Please consider sharing a link to https://RobotWritersAI.com from your blog, social media post, publication or emails. More links leading to RobotWritersAI.com helps everyone interested in AI-generated writing.

–Joe Dysart is editor of RobotWritersAI.com and a tech journalist with 20+ years experience. His work has appeared in 150+ publications, including The New York Times and the Financial Times of London.

The post U.S./China AI Competition: appeared first on Robot Writers AI.