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The proposed method accelerates training and enables stable policies where matched raw-action PPO remains near failure, with successful sim-to-real transfer in in-hand and arm-hand tasks.
R2S-Eval reveals that automated evaluation can outperform manual success counting by capturing nuanced differences in robot behavior quality.
World models can significantly enhance the stability and performance of humanoid robots in complex loco-manipulation tasks, outperforming traditional methods.
