Search papers, labs, and topics across Lattice.
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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.
Adaptive execution horizons can boost task success rates by over 11% while reducing runtime overhead in Vision-Language-Action models.
Foundation models can revolutionize hand-object interaction by systematically categorizing and leveraging diverse priors to enhance robot learning and task performance.
Force-based memory enables VLA models to achieve over 80% success in complex manipulation tasks, outperforming traditional memory methods with minimal computational cost.
