Light Origins trains Light-O1 on human video before adapting it to robots
Founder Roger Jiang says larger pretraining runs reduced held-out prediction errors after adaptation, though the study did not measure real-world task success at scale.
By RuntimeWire Staff · Published
Primary source: PR Newswire
Why it matters
Light-O1 tests whether internet video can reduce the cost of building robot foundation models. Its current evidence shows better held-out prediction after adaptation as pretraining scales, while real-world task success at scale remains unmeasured.
Light Origins, a company building foundation models for Physical AI, has released Light-O1, a model pretrained on structured human actions recovered from internet videos and then adapted to different robot bodies and tasks.…
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