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DenseReward synthesizes diverse failure trajectories automatically, enabling robots to learn from a rich array of failure modes without human labeling.
Pretraining action modules with motion priors can drastically enhance VLA model performance, achieving faster convergence and better success rates in complex robot manipulation tasks.
SurfBind's innovative surface-centric approach outperforms traditional methods, revealing the critical role of molecular surface interactions in epitope prediction.
