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This work proposes StageGuard, an agentic distillation framework for accurate and efficient stage-transition decisions, which combines teacher-model reasoning with demonstration trajectories to generate structured explanations of subtask completion and policy switching.
Current VLA benchmarks may be overstating real-world readiness, as models succeeding by standard metrics often exhibit unsafe behaviors and poor robustness.
Web-scale video pretraining lets robots handle real-world chaos better than vision-language models trained on curated robotics datasets.
