FAN: Foresight Action Normalization for Continual Adaptation of Vision-Language-Action Models

2026-09-18

AI Summary

研究通过提出FAN方法解决视觉-语言-动作模型在持续适应过程中因坐标漂移等问题导致的失败，提高模型长期部署中的稳定性和性能。

Abstract

Vision-Language-Action (VLA) models pre-trained on large-scale, closed datasets have demonstrated remarkable success across diverse robotic manipulation tasks. However, their long-term real-world deployment necessitates continuously acquiring new skills while retaining previously learned capabilities. While pioneering works have explored continual VLA adaptation using techniques such as experience replay and reinforcement fine-tuning, they overlook a foundational mechanism: action normalization, which determines the underlying coordinate system in which policies perceive and execute physical actions. To bridge this gap, we systematically evaluate five normalization strategies across four real-world task streams covering single-arm and bimanual manipulation. Our analysis reveals that existing protocols induce severe failure modes due to inter-task coordinate drift, limited motion coverage, or train-test coordinate mismatches. Motivated by these insights, we formulate three core design principles: consistency, coverage, and causality (3C), and introduce foresight action normalization (FAN). FAN estimates normalization statistics once from a small, task-independent calibration set prior to continual learning and freezes them throughout adaptation. Across all evaluated streams, FAN achieves the highest performance and demonstrates consistent robustness, providing insightful guidance for building stable action representations in achieving effective lifelong VLA adaptation.

Problem

• continual adaptation
• action normalization
• coordinate drift

Innovation

• Foresight Action Normalization
• continual learning
• action normalization
• 3C principles
• lifelong VLA adaptation