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Adapting motion priors to specific task contexts can dramatically enhance humanoid control performance and efficiency, outperforming traditional uniform approaches.
A new flow matching prior can drastically enhance humanoid motion tracking by guiding policy exploration with geometric insights from unordered pose data.
