MoE-DAgger: Transition-Aware Router-Entropy Gating for Selective Human Correction in Phase-Structured Manipulation
Overview
This paper introduces MoE-DAgger, a method that uses causal router history to distinguish normal phase transitions from errors in robot manipulation without explicit phase labels. The approach achieves 84.0% success on the Green task and 78.0% on the Red task under matched human-control budgets, outperforming phase-unaware filtered entropy by 22 percentage points on Green and 26 points on Red. No collisions were observed in 50 rollouts per task.
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- arXiv Robotics — research abstractsSignalMoE-DAgger: Transition-Aware Router-Entropy Gating for Selective Human Correction in Phase-Structured Manipulation
This paper introduces MoE-DAgger, a method that uses causal router history to distinguish normal phase transitions from errors in robot manipulation without explicit phase labels. The approach achieves 84.0% success on the Green task and 78.0% on the Red task under matched human-control budgets, outperforming phase-unaware filtered entropy by 22 percentage points on Green and 26 points on Red. No collisions were observed in 50 rollouts per task.
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