Humanoid Robots Can Sprint and Box, But Laundry Is the Real Test

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

But a less glamorous challenge may reveal more about whether humanoid robots are ready for everyday life: doing the laundry. Can a robot take a crumpled T-shirt from a basket, determine its orientation, fold it neatly, and recover when the sleeve slips from its hand?

No robot system can yet reliably handle the entire laundry process—from carrying a basket to the washing machine, sorting clothes for different programs, hanging them to dry, folding, and ironing. Even folding remains a challenge, as robots must cope with clothes of different shapes, sizes, materials, and levels of creasing.

Running fast is difficult. A humanoid must coordinate many joints, maintain balance, and generate powerful movements without falling. However, the task 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 soft and constantly changing shape. Pick up one corner of a T-shirt, and the rest of it moves.

Humans deal with this without much thought. They shake a shirt out, notice where the sleeves are, feel when the fabric slips, and adjust their 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 must notice when reality differs from its expectations and adapt. That is important because humanoid robots are increasingly discussed as future helpers in homes and care settings, as well as in 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.

There is another difficulty. Once one robot learns how to fold a shirt, it might seem obvious to 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 stay 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 what a shirt looks like, where its shoulders are likely to be, what a good fold looks like, or how to recognize 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.

The Humanoid Robot Games are valuable because they make robotics visible and show how quickly the technology is moving. But one should be careful about what those demonstrations prove. For robots intended to live and work alongside humans, 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 meters faster than the human world record is impressive. A robot that can take a messy basket of laundry, sort it, fold it, and recognize 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 is a Senior Lecturer in Electronics Robotics & Control Engineering at Cardiff Metropolitan University.

This article was originally published on The Conversation under a Creative Commons license.