Data Flywheel

The data flywheel is a self-reinforcing loop: robots act, generate data, learn from it, and act better. In humanoid robotics, this feedback cycle is core to improving perception, control, and decision-making over time. Each deployment, simulation, or failure creates logs, videos, and trajectories that feed the next round of training.

The flywheel effect accelerates with scale. Offline reinforcement learning, imitation learning, and foundation model fine-tuning all benefit from more and more diverse data. Many teams now operate dedicated ‘data factories’ — fleets of robots logging millions of real-world hours to feed the flywheel. 

The challenge is filtering for value: not all data teaches. The future of humanoids depends on how fast, and how well, they can learn from their own experience.

Data Flywheel at Humanoid