arXiv:2602.10104v2 Announce Type: replace-cross Abstract: Scaling action-controllable world models is limited by the scarcity of action labels. While latent action learning promises to extract control interfaces from unlabeled video, learned latents often fail to transfer across contexts: they entangle scene-specific cues and lack a shared coordinate system. This occurs because standard objectives operate only within each clip, providing no mechanism to align action semantics across contexts. Our key insight is that although actions are unobserved, their semantic effects are observable and can
The accelerating pace of AI research and the demand for more autonomous and adaptable AI systems drive the need for improved world models that can learn from limited data.
This research addresses a fundamental limitation in AI's ability to learn and transfer control policies across diverse situations, which is critical for scaling applications in robotics and synthetic environments.
The ability to orient latent actions for video world modeling suggests a pathway to more robust and generalized AI control, potentially reducing the need for extensive labeled datasets.
- · AI researchers
- · Robotics companies
- · Simulation platforms
- · Developers of autonomous systems
- · Companies reliant on large, hand-labeled action datasets
AI models will become more adept at learning common action semantics from unlabeled video data.
This improved latent action learning could accelerate the development and deployment of more capable autonomous AI agents.
Generalized AI control via unsupervised learning may reduce development costs and broaden AI application in complex, real-world scenarios.
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Read at arXiv cs.LG