Hierarchical Vision Language Action Model Using Success and Failure Demonstrations

Understand

Prior Vision-Language-Action (VLA) models are typically trained on teleoperated successful demonstrations, while discarding numerous failed attempts that occur naturally during data collection.

• However, these failures encode where and how policies can be fragile, information that can be exploited to improve robustness.
• We address this problem by leveraging mixed-quality datasets to learn failure-aware reasoning at planning time.
• We introduce VINE, a hierarchical vision-language-action model that separates high-level reasoning (System 2) from low-level control (System 1) under a hierarchical reinforcement learning formalism, making failures usable as a structured learning signal rather than noisy supervision.