Mingyu Ding

DenseReward synthesizes diverse failure trajectories automatically, enabling robots to learn from a rich array of failure modes without human labeling.

Substantial challenges in task generalization and visuomotor robustness are revealed, highlighting critical gaps in current dexterous manipulation benchmarks.