The paper introduces NAVA-WAM, a method for pretraining action policies directly from observation-only videos without relying on action-annotated robot data. It uses two stages: first, pretraining on videos with visual transition supervision, and second, post-training with action-labeled demonstrations to improve robot control.
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This approach enables more scalable and efficient training of robot action policies by leveraging observation-only videos, reducing dependency on labeled action data.
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