Weave Robotics just launched Isaac 0, an $7,999 laundry-folding robot that can't quite handle the job alone. The 18-month-old startup's first consumer product requires human teleoperators to assist with tricky folds, takes 30-90 minutes per load, and won't touch bed sheets, blankets, or inside-out clothes. It's a revealing glimpse at how far home robotics still has to go - and how startups are betting on iteration over perfection.
Weave Robotics is betting that Americans hate folding laundry enough to drop nearly eight grand on a robot that can't really do it alone. The Bay Area startup just opened reservations for Isaac 0, a stationary home robot with one job: folding your clothes while humans watch from afar, ready to jump in when things get complicated.
The pitch is straightforward. For $7,999 plus a $250 deposit, Bay Area residents get a plug-in folding station that tackles basic garments in 30 to 90 minutes per load. But the fine print tells a more interesting story about where consumer robotics actually stands in 2026. Isaac 0 won't touch large blankets, bed sheets, or anything turned inside-out, according to Weave's announcement. More revealing: it's not fully autonomous. The company stations teleoperators on standby to assist with trickier folds remotely.
That human-in-the-loop approach puts Isaac 0 in the same category as early self-driving car deployments - promising eventual autonomy while relying on remote safety drivers. It's a pragmatic strategy that lets Weave ship hardware now while the AI catches up. The company says performance will improve over time, suggesting Isaac 0 is as much a data collection device as a finished product.
The timing reflects broader momentum in home robotics. While companies like Tesla tease humanoid robots and Amazon experiments with warehouse bots, the path to useful home automation keeps hitting the same wall: manipulation of soft, unpredictable objects. Folding laundry is deceptively hard. Each fabric type behaves differently, clothes tangle and overlap, and identifying garment boundaries challenges even sophisticated computer vision systems.
