Think Before You Act: Teaching Robots to Imagine and Choose
FOREWARN framework combines world models with multimodal AI to give robots test-time intelligence, boosting success rates from 30% to 80% in real deployment.
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In recent years, foundation models have demonstrated remarkable capabilities in embodied intelligence. Through offline imitation learning, these embodied intelligence models have mastered diverse and complex manipulation skills, capable of completing various tasks such as grasping, carrying, and placing.
However, these models that "learn well" often "perform poorly" in real deployment: when facing environmental disturbances, task variations, or differences in user preferences, they tend to generate incorrect actions, leading to execution failures, as shown in the figure below:
This also exposes a core challenge in current embodied intelligence systems: how to enable robots to possess "reasoning capability" during deployment (Test-Time Intelligence), i.e., the ability to proactively predict risks and flexibly adjust strategies without requiring additional data.
To address this, a research team from Carnegie Mellon University and Berkeley Artificial Intelligence Research has proposed a novel framework called FOREWARN, which for the first time combines "world models" with "multimodal language reasoning" to perform online evaluation and dynamic correction of action policies generated through imitation learning during robot deployment, breaking the limitation of current embodied intelligence models that rely solely on offline imitation and taking an important step toward true deployment intelligence.