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NVIDIA's GEAR lab says its open Flora models give Astra-level robotics data labeling at 125x lower cost. The models already label 200K hours of video a day on 500 GPUs.
Flora is not a robot policy. It is a suite of open perception models that watch raw human and robot video. They label what happened (timestamped instructions) and how well it went (rewards and failure reasons). Yunze Man says the team uses Flora to train NVIDIA GR00T, so this labeling layer helps build the policy.
The weights are public, so the data engine is no longer a private advantage. Anyone with footage can build the same curation, reward and evaluation loop on it.
🔍 What Flora outputs
• Timestamped captions to curate and filter human egocentric and robot data
• Dense progress rewards for quality control and RL post-training
• Rubric scores for real-world benchmarking
• Failure reasons, so failed attempts become usable training data
📊 The numbers
• 125x lower cost at "Astra-level performance" (claimed)
• 200K hours of video labeled per day on 500 GPUs, in production at NVIDIA
• Works on human footage, which is cheaper to collect than robot data
⚠️ Caveats
• No benchmark, metric or cost breakdown supports the 125x figure, and Astra is never described
• The announcement gives no GPU type, model sizes, license or input specs
• Learned rewards could add bias or reward hacking to RL, and no accuracy numbers are published
• Every claim comes from the team that built it, so test it on your own data first
https://x.com/DrJimFan/status/2112597587987689912
If you train VLAs on egocentric video, which signal would you plug in first: captions for filtering, or progress rewards for RL?
• Medium: https://medium.com/p/2fd29045dffb
• Substack: https://sajjadpakdamansavoji.substack.com/p/nvidias-flora-puts-the-robot-data
• X: https://x.com/saavoji/status/2114758981474509137
• Website: https://sajjadpsavoji.github.io/blog#nvidia-just-opened
Flora is not a robot policy. It is a suite of open perception models that watch raw human and robot video. They label what happened (timestamped instructions) and how well it went (rewards and failure reasons). Yunze Man says the team uses Flora to train NVIDIA GR00T, so this labeling layer helps build the policy.
The weights are public, so the data engine is no longer a private advantage. Anyone with footage can build the same curation, reward and evaluation loop on it.
🔍 What Flora outputs
• Timestamped captions to curate and filter human egocentric and robot data
• Dense progress rewards for quality control and RL post-training
• Rubric scores for real-world benchmarking
• Failure reasons, so failed attempts become usable training data
📊 The numbers
• 125x lower cost at "Astra-level performance" (claimed)
• 200K hours of video labeled per day on 500 GPUs, in production at NVIDIA
• Works on human footage, which is cheaper to collect than robot data
⚠️ Caveats
• No benchmark, metric or cost breakdown supports the 125x figure, and Astra is never described
• The announcement gives no GPU type, model sizes, license or input specs
• Learned rewards could add bias or reward hacking to RL, and no accuracy numbers are published
• Every claim comes from the team that built it, so test it on your own data first
https://x.com/DrJimFan/status/2112597587987689912
If you train VLAs on egocentric video, which signal would you plug in first: captions for filtering, or progress rewards for RL?
• Medium: https://medium.com/p/2fd29045dffb
• Substack: https://sajjadpakdamansavoji.substack.com/p/nvidias-flora-puts-the-robot-data
• X: https://x.com/saavoji/status/2114758981474509137
• Website: https://sajjadpsavoji.github.io/blog#nvidia-just-opened
