Simple-to-Complex Demonstration Strategy Advances Vision-Language-Action Model Training从简到复杂的结构化演示策略改进视觉语言行动模型训练
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Researchers propose a structured demonstration collection strategy for Vision-Language-Action (VLA) models on a dual-arm robotic platform, addressing how demonstrations are organized for imitation learning.
The approach organizes data using three principles: decomposing complex tasks into progressively learnable sub-skills, standardizing interaction environments to reduce variability, and ordering demonstrations by increasing task complexity.
This structured design improves policy learning efficiency, training stability, and generalization by enabling VLA models to acquire fundamental manipulation skills before learning complex behaviors.