Helix Learns to Fold Laundry

The Helix VLA model architecture, previously applied to logistics tasks, has been successfully adapted to the complex dexterous manipulation challenge of folding laundry. This was achieved solely through data augmentation, without modifications to the model architecture or training hyperparameters. The post highlights Helix's ability to pick from mixed piles, adjust folding strategies based on initial configurations, recover from errors, and perform fine manipulation (edge tracing, pinching, unraveling) end-to-end from vision and language input to motor control. Additionally, Helix learned natural multimodal interactions such as maintaining eye contact, directing gaze, and using learned hand gestures.