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Video generative priors only translate into robust physical control when paired with explicit world-to-action information routing and synchronized joint denoising rather than standard monolithic fine-tuning.
Scheduling imagination in VLA models can cut GPU costs by 80% while boosting performance and robustness in real-world tasks.
Current autonomous AI agents are alarmingly unprepared for real-world adversarial attacks, often missing critical vulnerabilities in dynamic environments.
OneVLA unifies navigation and manipulation tasks into a single framework, enabling robots to seamlessly interpret commands and interact with their environments like never before.
