Track manipulation, locomotion and embodied systems.
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Directly learning robot action policies from raw video without explicit action labels unlocks scalable world action models.
Robots can now plan complex, long-horizon tasks in novel environments by predicting sparse visual sub-goals, achieving 70% success in zero-shot real-world experiments.
Semantic Affine Consistency transforms how visual tokenizers align latent semantics, resulting in a 26% reduction in gFID and setting a new benchmark for diffusion models.