Data

In humanoid robotics, data is what connects physical experience to model updates. Logs, trajectories, video, force feedback, and joint states drive the training of perception, control, and policy systems. But most robot data is narrow: it reflects structured environments, repeated tasks, or specific embodiments.

Data scarcity limits generalization, while lack of diversity can make models brittle. One-off demonstrations or simulation-only logs rarely transfer well. This has led to large-scale efforts to collect more varied, real-world data across robots, settings, and failure cases.

‘Data as is’ becomes the training truth. If what’s collected is biased or low quality, learning stalls. The next breakthroughs in humanoid intelligence may depend more on what data is used than how the model is designed.