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Being-H0.5 labels 16,000 hours of human video to train robot models

1 article1 sourcesince Nov 10Last article 2h ago ·

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Being-H0.5 researchers present a method that filters egocentric human video and labels it at two timescales for cross-embodiment robot learning.

The labels cover 1-second fine-grained motions and 10-second intent summaries, drawn from more than 16,000 hours of footage with Gemini video understanding. The work is presented as an oral (O1) and a poster (S1 #67), according to a post by Paige Bailey (@DynamicWebPaige).

Written by AI from the articles below · updated Nov 10, 9:34 AM ET

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Latest developmentNov 10, 9:27 AM ET
Being-H0.5 uses 16,000 hours of human video to scale robot learning

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Nov 10
  1. 👩‍💻 Paige BaileyX
    Being-H0.5 uses 16,000 hours of human video to scale robot learning

    AIBeing-H0.5 researchers present a method that filters egocentric human video and labels it at two timescales for cross-embodiment robot learning. The labels cover 1-second fine-grained motions and 10-second intent summaries, drawing on more than 16,000 hours of footage with Gemini video understanding. The work is presented as an oral (O1) and a poster (S1 #67).

    Image from @DynamicWebPaige's post

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