WorldBridge: Bridging Video and Action for Task Generalization in World Action Models
READ THE ORIGINAL SOURCEOpen original sourcehuggingface.co · Opens in a new tabThe paper studies task generalization for robots in World Action Models, separating video generalization (predicting visual evolution of new tasks) from action generalization (turning predictions into behavior via a video-to-action bridge). On RoboTwin 2.0, in-distribution video-action supervision gave 25.07% success on out-of-distribution tasks; adding action-free videos raised it to 55.90%, and one action-labeled demonstration per task raised it to 70.70%. The authors introduce WorldBridge, which uses separate video and action experts trained with joint flow matching, and report state-of-the-art results across several manipulation benchmarks.
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- Primary sourceHugging Face Daily Papers
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- Linked in sourceView arXiv page
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The paper studies task generalization for robots in World Action Models, separating video generalization (predicting visual evolution of new tasks) from action generalization (turning predictions into behavior via a video-to-action bridge). On RoboTwin 2.0, in-distribution video-action supervision gave 25.07% success on out-of-distribution tasks; adding action-free videos raised it to 55.90%, and one action-labeled demonstration per task raised it to 70.70%. The authors introduce WorldBridge, which uses separate video and action experts trained with joint flow matching, and report state-of-the-art results across several manipulation benchmarks.
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Key findings from the stored brief
- In-distribution video-action supervision yielded 25.07% success on out-of-distribution RoboTwin 2.0 tasks.
- Adding videos without action labels raised success to 55.90%; one action-labeled demo per task raised it to 70.70%.
- WorldBridge uses separate video and action experts connected through structured attention, trained with joint flow matching.
- WorldBridge reports 96.23% on RoboTwin 2.0 full data, 82.8% on RoboCasa GR1 TableTop, and 57.6% on DOMINO Level-1.
Source claims not independently checked; results are reported on specific simulated benchmarks and the comparison set is limited to the methods the authors chose.
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Why this may matter
The results suggest broader video coverage can enable robot task transfer by leveraging inverse-dynamics capabilities, and that sparse action labels further strengthen them. This points to a data-efficient route to generalizing robot policies to new tasks.
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