Figure’s Billion-Dollar Physical AI Bet Delivered A 6X Jump In Robot Chore Success

Four weeks ago Figure told the world it would spend a billion dollars over 12 months paying strangers to film themselves folding laundry, making beds and picking things up off the floor.

Just one month later, that money has already bought a 6X increase in humanoid robot performance: nine percent to 56%.

That’s the zero-shot success rate of Figure’s humanoid doing household chores in 30 rented Bay Area homes it had never been trained in, with and without pretraining on Index, the crowdsourced dataset those creators are filling. Without Index data: 9% success rate. With Index data: 56%. Same robot, same task data, same architecture, same optimizer, same hyperparameters, same blind evaluation.

Figure changed exactly one variable and success went up more than 6X.

That jump just might be the most important number in humanoid robotics this year, especially for robots intended to work in our homes. The number that got most of the attention was the 56% success rate, or its corollary: the 44% failure rate.

That’s fair: a 44% failure rate doesn’t get you an A in any school I’ve attended. But the success/failure rate tells you where humanoids are today: interesting but not amazing. The 6X boost tells you which direction they’re moving and why. It also tells you that Figure’s investment is already paying off.

And the four week time span tells you that change is happening quickly. (Note: Index was running for some time before Figure announced it, so the training data that enabled this improvement is for more than four weeks.)

In case you didn’t hear the details: Figure rented 30 homes, sent in its Figure 03 humanoid with no data collected in any of them, and graded a variety of whole-body behaviors — tidying 13 to 15 scattered toys into a basket, folding towels and placing them in a basket, and making a bed with both pillows and both comforter corners at the top and the comforter smoothed.

Compare that against previous baselines from just a few months ago.