Figure's Helix 2.5 Worked in 30 Homes It Had Never Seen. The Number That Matters Is 56%.

On 17 September 2026, Figure published results for Helix 2.5: a humanoid performing three household jobs across 30 Bay Area homes, with zero data collected in any of them.

Not thirty demos in a lab that looks like a home. Thirty real homes the robot had never been inside, with furniture it had never seen, in layouts nobody trained it on.

What actually happened

Figure adapted a single pretrained base model into three behaviours:

• -Tidying living rooms
• -Folding towels
• -Making beds

Together those span locomotion, rigid manipulation, deformable manipulation (a towel has no fixed shape), two-handed coordination, and active perception — looking around to decide what to do next. That combination is the point. Each one alone has been demonstrated before.

Why "unseen" is the whole story

Here is the thing people outside robotics consistently underrate: making a robot do a task is not the hard part. Making it do that task somewhere new is.

A robot trained in one kitchen learns that kitchen. Different light, a different counter height, a bedsheet with a different drape, and the policy falls apart. The standard fix is to go collect data in the new place and fine-tune. That does not scale to houses, because there are a billion houses and they are all different.

Zero-shot means none of that happened. No data collection in those 30 homes, no fine-tuning, no adaptation.

The number that makes it real

A roughly 6× improvement from pretraining alone — the task-specific training did not change, the base model did.

Two details underneath that I find more convincing than the headline:

Helix 2.5 used half the task-specific data of a comparable Helix 02 behaviour. Better pretraining bought them a cheaper fine-tune. That is the same curve language models walked, arriving in robotics.

No single evaluation task makes up more than 1.90% of the pretraining dataset. This is the detail that kills the obvious objection. If "making beds" were 30% of training, 56% would mean memorisation. At under 2%, the behaviour is coming from general pretraining, not from having seen the answer.

Where the data comes from

The base model is pretrained on Index, which Figure describes as a global-scale dataset of human behaviour currently generating roughly 35 minutes of new human experience every second.

Sit with that rate. It is the robotics answer to the internet-scale text corpus — the bet that you cannot hand-engineer household competence, you have to absorb it, and whoever absorbs the most wins. Figure announced Index on 25 August, three weeks before these results.

The honest limit

56% means it fails nearly half the time.

Figure says so themselves, plainly: "general humanoid robotics is not solved." Their evaluation also aborts a trial whenever a human has to step in for safety — so that 56% is measured under supervision, not in an empty house.

And in a home, the failure rate is not the only thing that matters — the failure mode is. A robot that makes a bed badly is a nuisance. A robot that pulls a shelf over is a different category of problem. Figure publishes no failure-mode breakdown, and the write-up does not specify which robot model ran the evaluations.

What this means in India

The Indian read on a home humanoid is different from the American one, and the difference is economic, not technical.

Household work here is already done, affordably, by people. A machine that succeeds 56% of the time and needs a human standing by for safety does not compete with that on cost or reliability — not now, and not at the price a humanoid will carry for years.

So the near-term Indian opportunity is not the home. It is everywhere the same generalization advance shows up first: warehouses, factory lines, and inspection work — structured, repetitive, supervised environments where a supervised 56% is a starting point rather than a disqualification, and where the labour is dangerous rather than cheap.

The thing to watch is not when a humanoid reaches your house. It is when this generalization curve meets a job that pays enough to absorb the failure rate.

What I could not verify

While researching this I found several sites confidently quoting a $25 per robot-operating-hour price for Figure 03 at BMW, plus a specific 40-unit fleet size. Neither figure appears anywhere on Figure's own news page. I am leaving both out, and I would be careful with any article that states them as fact.

Figure's own BMW post is dated 30 June 2026 and does not carry commercial pricing terms.

The question I cannot answer

What success rate makes a home robot actually tolerable?

It is clearly not 56%. But I do not think it is 99% either, because humans do not fold towels at 99% and we live with that fine. The real threshold probably has less to do with the success rate and more to do with what happens on the 5% — whether failure means a crooked duvet or a broken television.

Nobody is publishing that number. I would like to see it before I believe any home-robot timeline.