Figure may have found a scaling rule for robot pretraining: keep the model and task training fixed, add more Index data (Figure's large pretraining dataset of human behavior) and action-prediction loss falls in a clean, predictable way.
the smaller runs predicted the 8x-data run almost exactly, i.e. a robotics company can estimate what another doubling of human-behavior data will buy before spending the compute on the full run.
should make robot training less trial-and-error and more like LLM scaling, although the curve predicts action loss, not real-world task success.
so it does not yet prove equally predictable gains in robot reliability.
Figure just released this video. a beautiful robot future is indeed coming. Its Helix 2.5 model (Figure’s end-to-end robotics brain) lifted zero-shot household-...