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RLDX-1 achieves double the success rate of existing VLAs on complex humanoid tasks, suggesting a leap in robots' ability to handle contact-rich, dynamic manipulation.
Generate realistic raw camera images from text prompts, sidestepping the bottleneck of limited raw datasets and hardware constraints.
By validating generated actions against physics simulation, RoboCurate unlocks a new level of realism in synthetic robot training data, leading to massive performance gains on real-world manipulation tasks.
A 7B model trained with reinforcement learning beats GPT-4o on robotic reasoning tasks involving low-level action control.
