Nvidia's GEAR robotics lab open-sourced Flora on October 20: three models that turn robot and human manipulation video into the labels robot AI needs. Flora-TempSeg (9B) splits a video into action intervals, Flora-Captioner (27B) writes one short instruction for each interval, and Flora-Rewarder (27B) scores clips for outcome and process rewards, including against rubrics a user writes. All three are fine-tuned from Alibaba's Qwen3.5 models, with weights on Hugging Face under an Nvidia non-commercial research license.

Nvidia says Flora is the data engine used to train its GR00T humanoid models, labeling up to 200,000 hours of video a day across 512 GB300 chips. Its headline comparison is cost: about $0.72 to label an hour of video, against about $88.49 for GPT-6 Astra at maximum effort — roughly 125 times cheaper — with a 57.1 percent overall annotation score for Flora against 53.7 percent for Astra in Nvidia's own 50-episode evaluation.

Both figures need their caveats. The cost gap rests on Nvidia's modeled GPU pricing rather than measured production bills, the quality evaluation was run by Nvidia itself, and the results vary by task: Flora led on human videos and action boundaries, while Astra led on robot videos, caption accuracy and rubric scoring.