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R [31] and Splatt
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Transformer-based SLAM can achieve state-of-the-art accuracy and efficiency by integrating a spatially corrective back-end that restores high-cadence local bundle adjustment.
Training diffusion priors directly in a robot's motion space, using a mixture of human and robot data, unlocks more diverse and robust humanoid skills without manual intervention.
