Bridge-WA: Learning Action-Relevant World Dynamics for Robotic Manipulation
BRIDGE-WA is a lightweight framework that predicts future scene changes during robotic action to improve manipulation policies. It uses a frozen teacher to learn three world priors: intended outcomes, intervention support, and motion flow. These compact summaries guide the policy, allowing it to focus on causal dynamics rather than irrelevant visual details.