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Figure's Helix 2.5: Zero-Shot Home Robots

Figure released Helix 2.5, demoing humanoid robots working zero-shot in 30 unfamiliar homes and completing 56% of household tasks, drawing wide attention to embodied AI generalization.

2026-09-18 ~ 2026-09-22 · 2 episodes · 31 posts

Episode 1 · Figure Unveils Helix 2.5: Zero-Shot Household Work Across 30 Unseen Homes (2026-09-18, 27 posts)

On Sept 18, Figure launched Helix 2.5, its strongest humanoid foundation model to date, alongside a roughly 4-hour raw recording (later condensed to 30 minutes) of robots working zero-shot in 30 never-before-seen rental homes across the Bay Area. Without any additional training or on-site data collection, the robots performed chores such as tidying living rooms, folding towels, making beds, and picking up toys. The headline quantitative result: Index pretraining lifted zero-shot whole-body success from 8% to 56%.

Confirmed

  • Helix 2.5 is Figure's most powerful neural network and the first model trained on the Index platform, powering the Figure 03 robot; Index produced training data at 35 minutes of data per second of shooting, per Figure engineer coreylynch.
  • The model executes three whole-body behaviors zero-shot across 30 real Bay Area homes, with no training data collected in those homes; tasks include laundry, bed-making, towel folding, living-room tidying, and toy pickup.
  • Deployment is zero-shot into unfamiliar homes: no on-site fine-tuning. CEO Brett Adcock called generalization to unseen environments the "holy grail of robotics," framing Helix 2.5 as answering whether humanoids can enter never-before-seen homes and immediately start autonomous whole-body work.
  • The key ablation (which coreylynch repeatedly called the most important result): with identical model, task data, and evaluation, zero-shot whole-body success was 8% without Index pretraining versus 56% with it; under the strict criterion of fully completing a task, the gain exceeds 6x per rohanpaulai's summary.
  • Index shows predictable scaling: doubling data yields gains in next-action prediction predictable enough to forecast the final run's loss to decimal precision before training begins.
  • Watching the long-horizon live demos, coreylynch noted the experience changes qualitatively when perception and self-correction are strong enough—the model proactively finishes the task.
  • The raw video honestly shows which parts of zero-shot technology work in real homes and which still struggle; authors such as mhdfaran praised this transparency as worth emulating.

Why it matters

  • Zero-shot deployment validated across homes with no environment data is a key step from lab to general-purpose home robots; the 8%→56% ablation and predictable scaling curve provide rare quantitative evidence for scaling generalization via data volume.
  • Cautionary voices exist: Waymo research lead mwulfmeier praised the work highly while noting such systems are not yet deployable, with discussion emphasizing long-tail reliability as the real challenge. Optimists like Linus Ekenstam predict home robotics could produce a ten-trillion-dollar company within a decade—a personal opinion.

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Episode 2 · Figure's Helix 2.5 Does 56% of Chores Zero-Shot in 30 Unfamiliar Homes (2026-09-21, 4 posts)

Figure released Helix 2.5, its strongest humanoid robot system, which completed 56% of household chores end-to-end in 30 rented, never-before-seen Bay Area homes with zero additional training.