The future is robotics (data)Toggle theme← back<br>The future is robotics (data)

August '26

Sunday Robotics' Memo folding, ACT-2 preview1X's NEO hand, NEO's hands

Up until a few weeks ago, I was sceptical that robotics would have a major impact on the world anytime soon.<br>I saw robotics as much more complex than language models, and projecting the success of language models onto robotics seemed wrong.<br>Language models have an enormous dataset, i.e. the internet, to learn from for "free". Robotics does not.

However, in the past few weeks, my perspective has shifted considerably, and I am now 80% certain that we'll have robots capable of doing many household chores with high accuracy (>99%) in 1-2 years.<br>If that's achieved, there should be few reasons not to deploy them at scale in households in the US in the years thereafter [1].<br>And what can be done in homes should be possible to do in industry as well - at least for tasks currently done by humans - given that many industry tasks are more constrained than household tasks.

What changed my mind?

Sunday Robotics' 99.1% success rate for folding laundry, achieved mostly through careful data scaling efforts.

Scaling laws from human video data by Dyna.

1X's NEO hand dexterity videos.

The amount of money going into the robotics domain. Although not a guarantee for success, pouring lots of money and talent into a domain will typically result in a lot of progress. The past few weeks have been a whirlwind of one robotics announcement after another [2].

Points 1 and 3 will likely merge, and we will see large-scale, in-the-wild data acquisition for signals that are useful for training fine-grained hand manipulation [3].

My high-level argument is that 1) task-level reliability can be solved given enough data, 2) scaling laws are beginning to emerge, and 3) because data collection can be funded by capital, capital will drive improvements.

Folding clothes generalisation

Sunday Robotics' 99.1% success rate is amazing; few labs report success rates like these on tasks that are as broad as 'folding clothes', in different homes without per-home fine-tuning. When thinking about success rates like these, think about the number of errors per xxx trials: 90% is 1 error every 10 trials, 99% is 1 in 100. A subsequent goal might be to get to 99.9%; another 10x improvement over 99%: 1 error for every 1000 trials.

But does this 99.1% success rate for clothes folding mean that we can expect good performance on other household tasks? Let's unpack this. The report stated the following:

Across 9 garment types

In unseen homes without fine-tuning on that environment

Humans rated 'fold quality' at 4.72 out of 5 stars

Clothes in piles, in baskets, or on the floor, different lighting and a size range from 8XL shirts down to baby clothes.

As such, this seems to be a pretty convincing result; this is not a test done in a single environment for a very singular task. But even a robust result on a single task does not mean guaranteed results on other, different tasks. For one, it is easy to see that folding clothes is simpler than many other tasks. Folding has a few unique characteristics, as Chris Paxton noted in August '25:

It's fundamentally about predicting sequences of largely non-interactive trajectories, meaning that there's minimal force being applied at any given time.

You need a good amount of precision, but not too much precision. It's very forgiving of small mistakes, and you can use really low-cost hardware

It's very repeatable, and very easy to reset — practically self-resetting — making it a great test task for an autonomy stack, since you can collect a lot of data very quickly.

Let's look at a different household task: loading a dishwasher. Although it is somewhat repeatable (especially if you combine loading with unloading), it requires interactions with objects where force requirements vary (a rubber container deforms more than a glass cup). You likely need more precision, as failing in real scenarios means that something might break. You do not want to use a dishwasher loading robot that breaks 1 of every 100 cups it puts into your dishwasher [4]. So, 99% accuracy for loading a dishwasher is not enough, and the task itself is more challenging. This means that a) we will need more data and b) we will need even better performance, likely requiring even more data.

And 'more data' is a general statement; you need high-quality, diverse data at scale, which is not easy for tasks with many degrees of freedom. However, the household is a great place to collect that data (see again Chris Paxton, here).

There is more reason not to expect sudden generalisation to other tasks; DeepMind Robotics recently presented somewhat meagre results, albeit on difficult tasks, and with a very different approach:

Gemini Robotics 2 on Apollo with Sharpa hands: only unscrewing a bulb clears 90%; the other four tasks land between 32% and 44%.

Data scaling

Nevertheless, I remain...