Achieving the broad task generalization of embodied models in physical environments remains a central challenge in robot learning. We study this challenge in World Action Models (WAMs) through two com
WorldBridge improves robot task generalization by combining video prediction with sparse action supervision via separate video and action experts, achieving state-of-the-art results across manipulation benchmarks.
Achieving the broad task generalization of embodied models in physical environments remains a central challenge in robot learning. We study this challenge in World Action Models (WAMs) through two complementary capabilities: video generalization, which predicts the visual evolution of new tasks, and action generalization, which translates these predictions into executable 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.…
Summary and abstract excerpt via Hugging Face Papers ↗ · arXiv.
WorldBridge: Bridging Video and Action for Task Generalization in World Action Models was first seen on Hugging Face · Daily papers on Nov 2, 2026. Since then it has appeared in 11 checks on 2 different days. Its best position was #18 of 30, reached on Nov 3, 03:28 UTC. It dropped off the list after Nov 3, 03:28 UTC.
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It was first seen on Nov 2, 2026 and has been on the list on 2 days (11 checks) since.