WorldBridge: Bridging Video and Action for Task Generalization in World Action Models
Overview
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.
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Data origin: robot trajectories
“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”
Exact source · revision 1Source owner not reported
Reported quantity · Reported success rate: 25.07 percent · Basis not reported · Differing source assertions
“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”
Exact source · revision 1Source owner not reported
Reported quantity · Reported success rate: 55.9 percent · Basis not reported · Differing source assertions
“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”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalWorldBridge: 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.
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