# Helix Learns to Fold Laundry

August 12, 2025

Helix, Figure’s [Vision Language Action (VLA) model](/content/news/helix/index.html), recently demonstrated an hour of fully autonomous package reorientation in a logistics setting. Now, the same model is tackling something entirely different: folding laundry.

Folding laundry sounds mundane for a person, but this is one of the most challenging dexterous manipulation tasks for a humanoid robot. Towels are deformable, constantly changing shape, bending unpredictably, and prone to wrinkling or tangling. There’s no fixed geometry to memorize, and no single “correct” grasp point. Even a slight slip of a finger can cause the material to bunch or fall. Success requires more than just seeing the world accurately - it demands fine, coordinated finger control to trace edges, pinch corners, smooth surfaces, and adapt in real time.

**Key Results:**

- **A first for humanoids.** This is the first instance of a humanoid robot with multi-fingered hands folding laundry fully autonomously using an end-to-end neural network.

- **Same architecture, data-only change.** The same Helix architecture that [solved logistic tasks](/content/news/scaling-helix-logistics/index.html) was applied directly to laundry folding - with no modifications to the model or training hyperparameters. The only addition was the dataset.

- **Natural multimodal interaction.** In addition to folding, Helix learned to maintain eye contact, direct its gaze, and use learned hand gestures while engaging with people.

Video 1: Helix folds towels autonomously.

**Without any architectural changes, Helix learned to:**

- Pick towels from a mixed pile.
- Adjust folding strategies based on starting configurations.
- Recover from multi-pick errors by returning extra items.
- Use fine manipulation skills, like tracing an edge with a thumb, pinching corners, or unraveling tangled towels - before completing folds.

Critically, Helix does all of this without explicit object-level representations. For highly deformable items like towels, building such representations is brittle and unreliable. Instead, Helix operates entirely end-to-end: from vision and language input to smooth, precise motor control.

**Why this matters**

The same general-purpose architecture, and the same physical platform, can seamlessly transition from industrial logistics to household chores. As we scale real-world data collection, we expect Helix’s dexterity, speed, and generalization to keep improving across an even broader range of tasks.