Nvidia 开源 Flora 使125x的机器人视频标签比GPT-6 Astra便宜
将一个小时的机器人视频标注为72美分而不是88美分，这是Nvidia与其开放数据引擎弗洛拉承诺的。 但与GPT-6 Astra的比较并不像标价所暗示的那样偏差。
机器人的大型AI模型学习需要文字说明的视频，根据Nvidia的GEAR研究实验室，这需要三种标签。 首先，描述视频中发生什么动作，用来选择培训数据。 其次，对机器人的收视率如何一步步地进步，这样它就可以通过试验和错误来学习。 第三，判断一项任务是否成功完成，用于在部署期间监测机器人。 大多数现有工具只提供其中一种类型。 GPT-6 Astra等大型语言模型可以生成数个，但是它们对于所涉及的大量视频量来说过于昂贵。
根据论文，弗洛拉覆盖了全部三个。 Nvidia 称系统已经在训练其GR00T型号的过程中运行了，在512 GB300 GPU上标注了每天高达20万小时的视频。 这些模型可在Hugging Face上找到。
Flora由3个微调的Qwen3.5型号组成。 一个9B模型将视频分解为动作片段，一个27B模型在考虑每个动作前后发生的事情的同时对其进行了描述。 第二个叫"花-奖励"(Flora-Rewarder)的27B型机车根据出场时间的不同承担出三个角色。 它以1到5的尺度来评分一集的成功率，将每分半秒的步分归为正常，次优，或失败等，并根据自定义的分数来分配分数。
根据Nvidia自己的估计，最高125x更便宜
在有50个视频的测试中，弗洛拉的分数比GPT-6 Astra根据Nvidia的分数略高，尽管OpenAI的模型被允许使用其全部推理能力。 按Nvidia的估计，标注一个小时的视频与弗洛拉运行了约72美分，而与GPT-6 Astra运行的视频约为88.50. Claude 电话 Opus 5在同一测试中得分更差，仍然每小时花费约42美元。
然而，弗洛拉的成本数字是预测，而不是生产计量。 Nvidia根据每小时每GPU10美元的假定租金价格来计算。
Flora 发现动作更好，但Astra经常更准确地描述
在公共WGO-Bench上，弗洛拉最能探测出动作边界，但在整体分数中追上Astra，在纯描述精度上落在Gemini 3.8 Flash后。 研究人员认为，这些收益主要来自时间局部化。
在Nvidia自己的DenseCaption Bench上，弗洛拉带领所有度量衡进行人类录音，但对于机器人视频来说，模型在Astra后排第二。 研究者也承认了一种方法 GPT-6 坐标 Astra评价描述，同时也是被测试的模型之一。
几乎没有其他模型发现中段错误
在Robo Reward Bench上，弗洛拉的出错率比Astra低了27.8%. 文件将空白归结为部分完成的任务。 当一个机器人只给一瓶酒倒过来而不是把它从桌子上敲下来时，弗洛拉在5分中分配出3分与人类的参考值相匹配，而Astra只给出了1分。
Flora-Rewarder在一集中捕获了53.3%的失败步骤，而没有竞争对手获得超过11.7%的成绩，而Astra则一集都没有捕获。 权衡的是，弗洛拉错误地标出大约11%的正常步骤为错误。 Astra仍然领先于基于路由的得分和轨迹排名。
Flora在一段有许多短手动作的视频中，确定了37起个人行动中的34起，而Astra只捕获了4起。 在反面，弗洛拉有时会将更长的动作分成太多的小步骤，偶尔也会得到错误的描述。 在一个视频中，一款机器人手用布擦一盘盘子，但弗洛拉形容为折叠了餐巾。
Nvidia将弗洛拉定位为Scale AI物理AI数据引擎等商业服务的开放替代品。 研究人员可以使用它来放大他们自己的机器人数据集和测试方法，如将人类演示转移到机器人上。 Nvidia投资了数十亿的开放AI模型，让开发者保持其硬件生态系统并驱动对其芯片的需求，而弗洛拉服务于这个目标。
该公司还进行了一段时间的工作，通过模拟和自动化数据处理来弥补机器人培训数据的短缺。 在今年早些时候的GTC,Nvidia展示了自动准备，乘以并评分培训材料的数据管道。 Flora 新增详细标签和奖励信号从视频到管道。
所有这些都为Nvidia的涵盖整个机器人价值链的战略提供了信息，从硬件和培训数据到模拟和AI模型本身。 兹克 2Z最近的宣布 Gemini 4 Argon以机器人训练过程的类似部分为目标，同时也确定了训练视频中的工作步骤并标出失败的行动。
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Nvidia open-sources Flora to make robot video labeling 125x cheaper than GPT-6 Astra
Labeling one hour of robot video for 72 cents instead of $88 is what Nvidia promises with Flora, its open data engine. But the comparison with GPT-6 Astra isn't quite as lopsided as the price tag suggests.
Large AI models for robots learn from videos that need text annotations, and according to Nvidia's GEAR research lab, this requires three types of labels. First, descriptions of what action happens when in the video, used to select training data. Second, ratings of how well a robot progresses step by step, so it can learn through trial and error. And third, judgments on whether a task was completed successfully, used to monitor robots during deployment. Most existing tools only provide one of these types. Large language models like GPT-6 Astra can generate several, but they're too expensive for the massive volumes of video involved.
According to the paper, Flora covers all three. Nvidia says the system is already running during training of its GR00T models, annotating up to 200,000 hours of video per day across 512 GB300 GPUs. The models are available on Hugging Face.
Flora consists of three fine-tuned Qwen3.5 models. A 9B model splits videos into action segments, and a 27B model describes them while factoring in what happened before and after each action. A second 27B model called Flora-Rewarder takes on three roles depending on the prompt. It rates an episode's success on a scale of 1 to 5, classifies each half-second step as normal, suboptimal, or failure, and assigns scores based on self-defined rubrics.
Up to 125x cheaper by Nvidia's own estimates
In a test with 50 videos, Flora scored slightly better than GPT-6 Astra according to Nvidia, even though OpenAI's model was allowed to use its full reasoning capacity. Labeling one hour of video runs about 72 cents with Flora by Nvidia's estimate, compared to roughly $88.50 with GPT-6 Astra. Claude Opus 5 scored much worse in the same test and still cost about $42 per hour.
The Flora cost figures are projections, though, not production measurements. Nvidia calculated them based on an assumed rental price of $10 per GPU per hour.
Flora finds actions better, but Astra often describes them more precisely
On the public WGO-Bench, Flora is best at detecting action boundaries but trails Astra in overall score and falls behind Gemini 3.8 Flash in pure description accuracy. According to the researchers, the gains come mainly from temporal localization.
On Nvidia's own DenseCaptionBench, Flora leads across all metrics for human recordings, but for robot videos the model comes in second behind Astra. There's a methodological catch the researchers acknowledge, too. GPT-6 Astra evaluates the descriptions while also being one of the models under test.
Almost no other model catches mid-episode errors
On RoboRewardBench, Flora's error rate is 27.8 percent lower than Astra's. The paper attributes the gap to partially completed tasks. When a robot only tips a bottle over instead of knocking it off the table, Flora assigns 3 out of 5 points to match the human reference, while Astra gives just one.
Flora-Rewarder catches 53.3 percent of failed steps within an episode, while no competitor gets above 11.7 percent and Astra doesn't catch a single one. The tradeoff is that Flora falsely flags about 11 percent of normal steps as errors. Astra still leads on rubric-based scoring and trajectory ranking.
In a video with many short hand movements, Flora identifies 34 of 37 individual actions, while Astra catches only four. On the flip side, Flora sometimes splits longer actions into too many small steps and occasionally gets descriptions wrong. In one video, a robot hand wipes a plate with a cloth, but Flora describes it as folding a napkin.
Nvidia positions Flora as an open alternative to commercial services like Scale AI's Physical AI Data Engine. Researchers can use it to scale their own robotics datasets and test approaches like transferring human demonstrations to robots. Nvidia is investing billions in open AI models to keep developers in its hardware ecosystem and drive demand for its chips, and Flora serves that same goal.
The company has also been working for some time to offset the shortage of robotics training data through simulation and automated data processing. At GTC earlier this year, Nvidia showed a data pipeline that automatically prepares, multiplies, and scores training material. Flora adds detailed labels and reward signals from video to that pipeline.
All of this feeds into Nvidia's strategy of covering the entire robotics value chain, from hardware and training data to simulation and the AI models themselves. Google's recently announced Gemini 4 Argon targets a similar part of the robotics training process, also identifying work steps in training videos and flagging failed actions.
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