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Achieving dense metric depth completion from sparse dToF sensors, our framework outperforms state-of-the-art methods while being trained solely on synthetic data.
Force-based memory enables VLA models to achieve over 80% success in complex manipulation tasks, outperforming traditional memory methods with minimal computational cost.
