WorldBridge: Bridging Video and Action for Task Generalization in World Action Models
WorldBridge: Bridging Video and Action for Task Generalization in World Action Models
This research presents WorldBridge, a model designed to enhance task generalization in embodied AI by bridging video prediction with actionable behavior. The study evaluates the contributions of video supervision versus action-labeled demonstrations on benchmarks such as RoboTwin 2.0, showing that sparse action labels significantly improve performance over video-only inputs. WorldBridge achieves state-of-the-art results across multiple manipulation tasks using a unified pipeline that handles heterogeneous robot trajectories and egocentric data.
The paper introduces WorldBridge, a World Action Model that combines video prediction and action grounding to improve task generalization on embodied robots, reporting specific success rates on benchmarks like RoboTwin 2.0 under varying supervision conditions.
Source: arXiv Robotics — research abstracts · Read original article ↗
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What the source reports
Publisher-reported claims, with original evidence. These results have not been independently verified by RoboSignal.
- Data origin
- Robot trajectoriesOpen source S5
Reported numbers
Reported success rate
25.07%
View original evidence
le behavior through a video-to-action bridge. By varying video and action supervision for tasks outside an initial training set, we examine their respective contributions to task transfer. On RoboTwin 2.0, using only in-distribution video-action supervision yields a 25.07% success rate on out-of-distribution tasks. Introducing videos of these tasks without their action labels raises success to 55.90%, while adding ju
Open source S3Reported success rate
55.9%
View original evidence
le behavior through a video-to-action bridge. By varying video and action supervision for tasks outside an initial training set, we examine their respective contributions to task transfer. On RoboTwin 2.0, using only in-distribution video-action supervision yields a 25.07% success rate on out-of-distribution tasks. Introducing videos of these tasks without their action labels raises success to 55.90%, while adding ju
Open source S3
- paper: not_reported
What remains unknown
Not established in the collected evidence: Environment, Control.
Reported performance applies to the described task. It does not establish general autonomy or deployment readiness.
Source excerpts and review record
Automatically extracted; no manual editorial approval recorded.
d trained with joint flow matching. WorldBridge incorporates visual memory at two temporal scales and action forcing, alongside a unified data processing pipeline that standardizes and filters heterogeneous robot trajectories and egocentric hand-derived supervision. Across multiple manipulation benchmarks, WorldBridge achieves state-of-the-art performance among the compared methods, including 96.23% on RoboTwin 2.0 f
Open source S5
le behavior through a video-to-action bridge. By varying video and action supervision for tasks outside an initial training set, we examine their respective contributions to task transfer. On RoboTwin 2.0, using only in-distribution video-action supervision yields a 25.07% success rate on out-of-distribution tasks. Introducing videos of these tasks without their action labels raises success to 55.90%, while adding ju
Open source S3
st one action-labeled demonstration per task further improves it to 70.70%. These findings suggest that broader video coverage enables task transfer by leveraging existing inverse-dynamics capabilities, while even sparse action supervision further strengthens those capabilities. Guided by this perspective, we introduce WorldBridge, a WAM with separate video and action experts connected through structured attention an
Open source S4
Implications for data suppliers
RoboSignal interpretation and collection questions, not statements of buyer demand.
- Match the buyer’s robot embodiment, action space, sensors and timestamp requirements before planning collection.
- Compare the reported units and scope before using these quantities in a budget. Recording hours, sensor-hours and trajectories are different measures.
- Validate demand and acceptance criteria with a buyer before scaling. Publication, popularity and a research result do not establish a purchase commitment.
Source：arXiv Robotics — research abstracts · arxiv.org